diff --git a/notebooks/T1 - DA & EnKF.ipynb b/notebooks/T1 - DA & EnKF.ipynb
index 995aa0c..45b9a46 100644
--- a/notebooks/T1 - DA & EnKF.ipynb	
+++ b/notebooks/T1 - DA & EnKF.ipynb	
@@ -9,7 +9,7 @@
     "*Copyright (c) 2020, Patrick N. Raanes\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
diff --git a/notebooks/T2 - Gaussian distribution.ipynb b/notebooks/T2 - Gaussian distribution.ipynb
index 9767176..385597e 100644
--- a/notebooks/T2 - Gaussian distribution.ipynb	
+++ b/notebooks/T2 - Gaussian distribution.ipynb	
@@ -38,7 +38,7 @@
     "# T2 - The Gaussian (Normal) distribution\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$\n",
     "Computers generally represent functions *numerically* by their values on a grid\n",
     "of points (nodes), an approach called ***discretisation***.\n",
@@ -64,14 +64,14 @@
    "metadata": {},
    "source": [
     "## The univariate (a.k.a. 1-dimensional, scalar) case\n",
-    "Consider the Gaussian random variable $x \\sim \\mathcal{N}(\\mu, \\sigma^2)$.  \n",
+    "Consider the Gaussian random variable $x \\sim \\NormDist(\\mu, \\sigma^2)$.  \n",
     "Its probability density function (**pdf**),\n",
     "$\n",
-    "p(x) = \\mathcal{N}(x \\mid \\mu, \\sigma^2)\n",
+    "p(x) = \\NormDist(x \\mid \\mu, \\sigma^2)\n",
     "$ for $x \\in (-\\infty, +\\infty)$,\n",
     "is given by\n",
     "$$\\begin{align}\n",
-    "\\mathcal{N}(x \\mid \\mu, \\sigma^2) = (2 \\pi \\sigma^2)^{-1/2} e^{-(x-\\mu)^2/2 \\sigma^2} \\,. \\tag{G1}\n",
+    "\\NormDist(x \\mid \\mu, \\sigma^2) = (2 \\pi \\sigma^2)^{-1/2} e^{-(x-\\mu)^2/2 \\sigma^2} \\,. \\tag{G1}\n",
     "\\end{align}$$\n",
     "\n",
     "Run the cell below to define a function to compute the pdf (G1) using the `scipy` library."
@@ -157,7 +157,7 @@
    "id": "94b6d541",
    "metadata": {},
    "source": [
-    "**Exc -- Derivatives:** Recall $p(x) = \\mathcal{N}(x \\mid \\mu, \\sigma^2)$ from eqn (G1).  \n",
+    "**Exc -- Derivatives:** Recall $p(x) = \\NormDist(x \\mid \\mu, \\sigma^2)$ from eqn (G1).  \n",
     "Use pen, paper, and calculus to answer the following questions,  \n",
     "which derive some helpful mnemonics about the distribution.\n",
     "\n",
@@ -201,7 +201,7 @@
    "metadata": {},
    "source": [
     "#### Exc (optional) -- Integrals\n",
-    "Recall $p(x) = \\mathcal{N}(x \\mid \\mu, \\sigma^2)$ from eqn (G1). Abbreviate it using $c = (2 \\pi \\sigma^2)^{-1/2}$.  \n",
+    "Recall $p(x) = \\NormDist(x \\mid \\mu, \\sigma^2)$ from eqn (G1). Abbreviate it using $c = (2 \\pi \\sigma^2)^{-1/2}$.  \n",
     "Use pen, paper, and calculus to show that\n",
     " - (i) the first parameter, $\\mu$, indicates its **mean**, i.e. that $$\\mu = \\Expect[x] \\,.$$\n",
     "   *Hint: you can rely on the result of (iii)*\n",
diff --git a/notebooks/T3 - Bayesian inference.ipynb b/notebooks/T3 - Bayesian inference.ipynb
index 53a9fac..0e2762c 100644
--- a/notebooks/T3 - Bayesian inference.ipynb	
+++ b/notebooks/T3 - Bayesian inference.ipynb	
@@ -35,7 +35,7 @@
     "Now that we have reviewed some probability, we can look at statistical inference.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
@@ -48,7 +48,7 @@
     "studied the Gaussian probability density function (pdf), defined by:\n",
     "\n",
     "$$\\begin{align}\n",
-    "\\mathcal{N}(x \\mid \\mu, \\sigma^2) &= (2 \\pi \\sigma^2)^{-1/2} e^{-(x-\\mu)^2/2 \\sigma^2} \\,,\\tag{G1} \\\\\n",
+    "\\NormDist(x \\mid \\mu, \\sigma^2) &= (2 \\pi \\sigma^2)^{-1/2} e^{-(x-\\mu)^2/2 \\sigma^2} \\,,\\tag{G1} \\\\\n",
     "\\NormDist(\\x \\mid  \\mathbf{\\mu}, \\mathbf{\\Sigma})\n",
     "&=\n",
     "|2 \\pi \\mathbf{\\Sigma}|^{-1/2} \\, \\exp\\Big(-\\frac{1}{2}\\|\\x-\\mathbf{\\mu}\\|^2_\\mathbf{\\Sigma} \\Big) \\,, \\tag{GM}\n",
@@ -476,12 +476,12 @@
     "In response to this computational difficulty, we try to be smart and do something more analytical (\"pen-and-paper\"): we only compute the parameters (mean and (co)variance) of the posterior pdf.\n",
     "\n",
     "This is doable and quite simple in the Gaussian-Gaussian case, when $\\ObsMod$ is linear (i.e. just a number):  \n",
-    "- Given the prior of $p(x) = \\mathcal{N}(x \\mid x\\supf, P\\supf)$\n",
-    "- and a likelihood $p(y|x) = \\mathcal{N}(y \\mid \\ObsMod x,R)$,  \n",
+    "- Given the prior of $p(x) = \\NormDist(x \\mid x\\supf, P\\supf)$\n",
+    "- and a likelihood $p(y|x) = \\NormDist(y \\mid \\ObsMod x,R)$,  \n",
     "- $\\implies$ posterior\n",
     "$\n",
     "p(x|y)\n",
-    "= \\mathcal{N}(x \\mid x\\supa, P\\supa) \\,,\n",
+    "= \\NormDist(x \\mid x\\supa, P\\supa) \\,,\n",
     "$\n",
     "where, in the 1-dimensional/univariate/scalar (multivariate is discussed in [T5](T5%20-%20Multivariate%20Kalman%20filter.ipynb)) case:\n",
     "\n",
@@ -501,7 +501,7 @@
     "- (a) Actually derive the first term of the RHS, i.e. eqns. (5) and (6).  \n",
     "  *Hint: you can simplify the task by first \"hiding\" $\\ObsMod$ by astutely multiplying by $1$ somewhere.*\n",
     "- (b) *Optional*: Derive the full RHS (i.e. also the second term).\n",
-    "- (c) Derive $p(x|y) = \\mathcal{N}(x \\mid x\\supa, P\\supa)$ from eqns. (5) and (6)\n",
+    "- (c) Derive $p(x|y) = \\NormDist(x \\mid x\\supa, P\\supa)$ from eqns. (5) and (6)\n",
     "  using part (a), Bayes' rule (BR2), and the Gaussian pdf (G1)."
    ]
   },
@@ -522,11 +522,11 @@
    "source": [
     "**Exc -- Temperature example:**\n",
     "The statement $x = \\mu \\pm \\sigma$ is *sometimes* used\n",
-    "as a shorthand for $p(x) = \\mathcal{N}(x \\mid \\mu, \\sigma^2)$. Suppose\n",
+    "as a shorthand for $p(x) = \\NormDist(x \\mid \\mu, \\sigma^2)$. Suppose\n",
     "- you think the temperature $x = 20°C \\pm 2°C$,\n",
     "- a thermometer yields the observation $y = 18°C \\pm 2°C$.\n",
     "\n",
-    "Show that your posterior is $p(x|y) = \\mathcal{N}(x \\mid 19, 2)$"
+    "Show that your posterior is $p(x|y) = \\NormDist(x \\mid 19, 2)$"
    ]
   },
   {
diff --git a/notebooks/T4 - Time series filtering.ipynb b/notebooks/T4 - Time series filtering.ipynb
index b31faf9..867cac1 100644
--- a/notebooks/T4 - Time series filtering.ipynb	
+++ b/notebooks/T4 - Time series filtering.ipynb	
@@ -36,7 +36,7 @@
     "let's get more familiar with time-dependent (temporal/sequential) problems.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
@@ -208,7 +208,7 @@
     "Formulae (5) and (6) are called the **forecast step** of the KF.\n",
     "But when $y_1$ becomes available, according to eqn. (Obs),\n",
     "then we can update/condition our estimate of $x_1$, i.e. compute the posterior,\n",
-    "$p(x_1 | y_1) = \\mathcal{N}(x_1 \\mid x\\supa_1, P\\supa_1) \\,,$\n",
+    "$p(x_1 | y_1) = \\NormDist(x_1 \\mid x\\supa_1, P\\supa_1) \\,,$\n",
     "using the formulae we developed for Bayes' rule with\n",
     "[Gaussian distributions](T3%20-%20Bayesian%20inference.ipynb#Gaussian-Gaussian-Bayes'-rule-(1D)).\n",
     "\n",
diff --git a/notebooks/T5 - Multivariate Kalman filter.ipynb b/notebooks/T5 - Multivariate Kalman filter.ipynb
index baf7f04..edc79cf 100644
--- a/notebooks/T5 - Multivariate Kalman filter.ipynb	
+++ b/notebooks/T5 - Multivariate Kalman filter.ipynb	
@@ -38,7 +38,7 @@
     "Dealing with vectors and matrices is a lot like plain numbers. But some things get more complicated.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
diff --git a/notebooks/T6 - Geostats & Kriging (optional).ipynb b/notebooks/T6 - Geostats & Kriging (optional).ipynb
index 5802660..0781dfb 100644
--- a/notebooks/T6 - Geostats & Kriging (optional).ipynb	
+++ b/notebooks/T6 - Geostats & Kriging (optional).ipynb	
@@ -43,7 +43,7 @@
     "and their estimation.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
diff --git a/notebooks/T7 - Chaos & Lorenz (optional).ipynb b/notebooks/T7 - Chaos & Lorenz (optional).ipynb
index e7af91a..ffb726c 100644
--- a/notebooks/T7 - Chaos & Lorenz (optional).ipynb	
+++ b/notebooks/T7 - Chaos & Lorenz (optional).ipynb	
@@ -37,7 +37,7 @@
     "As opposed to the opinions of Descartes/Newton/Laplace, chaos effectively means that even in a deterministic (non-stochastic) universe, we can only predict \"so far\" into the future. This will be illustrated below using two toy-model dynamical systems made by ***Edward Lorenz***.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
diff --git a/notebooks/T8 - Monte-Carlo & ensembles.ipynb b/notebooks/T8 - Monte-Carlo & ensembles.ipynb
index cbb336a..036ca89 100644
--- a/notebooks/T8 - Monte-Carlo & ensembles.ipynb	
+++ b/notebooks/T8 - Monte-Carlo & ensembles.ipynb	
@@ -51,7 +51,7 @@
     "An ensemble is an *iid* sample. I.e. a set of \"members\" (\"particles\", \"realizations\", or \"sample points\") that have been drawn (\"sampled\") independently from the same distribution. With the EnKF, these assumptions are generally tenuous, but pragmatic.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$\n",
     "\n",
     "Ensembles can be used to characterize uncertainty: either by using it to compute (estimate) *statistics* thereof, such as the mean, median, variance, covariance, skewness, confidence intervals, etc (any function of the ensemble can be seen as a \"statistic\"), or by using it to reconstruct the distribution/density from which it is sampled. The latter is illustrated by the plot below. Take a moment to digest its code, and then answer the following exercises."
@@ -109,7 +109,7 @@
     "\n",
     "**Exc -- Multivariate Gaussian sampling:**\n",
     "Suppose $\\z$ is a standard Gaussian,\n",
-    "i.e. $p(\\z) = \\mathcal{N}(\\z \\mid \\bvec{0},\\I_{\\xDim})$,\n",
+    "i.e. $p(\\z) = \\NormDist(\\z \\mid \\bvec{0},\\I_{\\xDim})$,\n",
     "where $\\I_{\\xDim}$ is the $\\xDim$-dimensional identity matrix.  \n",
     "Let $\\x = \\mat{L}\\z + \\mu$.\n",
     "\n",
diff --git a/notebooks/T9 - Writing your own EnKF.ipynb b/notebooks/T9 - Writing your own EnKF.ipynb
index 8a42c58..7b71710 100644
--- a/notebooks/T9 - Writing your own EnKF.ipynb	
+++ b/notebooks/T9 - Writing your own EnKF.ipynb	
@@ -38,7 +38,7 @@
     "a forecast step and an analysis step.\n",
     "$\n",
     "% ######################################## Loading TeX (MathJax)... Please wait ########################################\n",
-    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathcal{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
+    "\\newcommand{\\Reals}{\\mathbb{R}} \\newcommand{\\Expect}[0]{\\mathbb{E}} \\newcommand{\\NormDist}{\\mathscr{N}} \\newcommand{\\DynMod}[0]{\\mathscr{M}} \\newcommand{\\ObsMod}[0]{\\mathscr{H}} \\newcommand{\\mat}[1]{{\\mathbf{{#1}}}} \\newcommand{\\bvec}[1]{{\\mathbf{#1}}} \\newcommand{\\trsign}{{\\mathsf{T}}} \\newcommand{\\tr}{^{\\trsign}} \\newcommand{\\ceq}[0]{\\mathrel{≔}} \\newcommand{\\xDim}[0]{D} \\newcommand{\\supa}[0]{^\\text{a}} \\newcommand{\\supf}[0]{^\\text{f}} \\newcommand{\\I}[0]{\\mat{I}} \\newcommand{\\K}[0]{\\mat{K}} \\newcommand{\\bP}[0]{\\mat{P}} \\newcommand{\\bH}[0]{\\mat{H}} \\newcommand{\\bF}[0]{\\mat{F}} \\newcommand{\\R}[0]{\\mat{R}} \\newcommand{\\Q}[0]{\\mat{Q}} \\newcommand{\\B}[0]{\\mat{B}} \\newcommand{\\C}[0]{\\mat{C}} \\newcommand{\\Ri}[0]{\\R^{-1}} \\newcommand{\\Bi}[0]{\\B^{-1}} \\newcommand{\\X}[0]{\\mat{X}} \\newcommand{\\A}[0]{\\mat{A}} \\newcommand{\\Y}[0]{\\mat{Y}} \\newcommand{\\E}[0]{\\mat{E}} \\newcommand{\\U}[0]{\\mat{U}} \\newcommand{\\V}[0]{\\mat{V}} \\newcommand{\\x}[0]{\\bvec{x}} \\newcommand{\\y}[0]{\\bvec{y}} \\newcommand{\\z}[0]{\\bvec{z}} \\newcommand{\\q}[0]{\\bvec{q}} \\newcommand{\\br}[0]{\\bvec{r}} \\newcommand{\\bb}[0]{\\bvec{b}} \\newcommand{\\bx}[0]{\\bvec{\\bar{x}}} \\newcommand{\\by}[0]{\\bvec{\\bar{y}}} \\newcommand{\\barB}[0]{\\mat{\\bar{B}}} \\newcommand{\\barP}[0]{\\mat{\\bar{P}}} \\newcommand{\\barC}[0]{\\mat{\\bar{C}}} \\newcommand{\\barK}[0]{\\mat{\\bar{K}}} \\newcommand{\\D}[0]{\\mat{D}} \\newcommand{\\Dobs}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\Dmod}[0]{\\mat{D}_{\\text{obs}}} \\newcommand{\\ones}[0]{\\bvec{1}} \\newcommand{\\AN}[0]{\\big( \\I_N - \\ones \\ones\\tr / N \\big)}\n",
     "$"
    ]
   },
diff --git a/notebooks/resources/HMM.tex b/notebooks/resources/HMM.tex
index e37c95f..9fa1c18 100644
--- a/notebooks/resources/HMM.tex
+++ b/notebooks/resources/HMM.tex
@@ -40,7 +40,7 @@
 \draw[arrow] (xkdots) to[bend left=15] node[midway, above] {$p(\x_K | \x_{K-1})$} (xK);
 
 % Observation model
-% \draw[arrow] (x0) to[bend left=15] node[pos=0.35, left] {$\mathcal{H}_0$} (y0);
+% \draw[arrow] (x0) to[bend left=15] node[pos=0.35, left] {$\mathscr{H}_0$} (y0);
 \draw[arrow] (x1) to[bend left=15] node[pos=0.35, right] {$p(\y_1 | \x_1)$} (y1);
 \draw[arrow] (xk) to[bend left=15] node[pos=0.35, right] {$p(\y_k | \x_k)$} (yk);
 \draw[arrow] (xK) to[bend left=15] node[pos=0.35, left, xshift=1mm] {$p(\y_K | \x_K)$} (yK);
diff --git a/notebooks/resources/answers.py b/notebooks/resources/answers.py
index 813b62e..53e8163 100644
--- a/notebooks/resources/answers.py
+++ b/notebooks/resources/answers.py
@@ -592,7 +592,7 @@ def setup_typeset():
 $$ \begin{align}
 p\, (y_1, \ldots, y_K \;|\; a)
 &= \prod_{k=1}^K \, p\, (y_k \;|\; a) \tag{each obs. is indep. of others, knowing $a$.} \\\
-&= \prod_k \, \mathcal{N}(y_k \mid a k,R) \tag{inserted eqn. (3) and then (1).} \\\
+&= \prod_k \, \NormDist{N}(y_k \mid a k,R) \tag{inserted eqn. (3) and then (1).} \\\
 &= \prod_k \,  (2 \pi R)^{-1/2} e^{-(y_k - a k)^2/2 R} \tag{inserted eqn. (G1) from T2.} \\\
 &= c \exp\Big(\frac{-1}{2 R}\sum_k (y_k - a k)^2\Big) \\\
 \end{align} $$
diff --git a/notebooks/resources/macros.py b/notebooks/resources/macros.py
index b1a6c9d..94fd705 100755
--- a/notebooks/resources/macros.py
+++ b/notebooks/resources/macros.py
@@ -16,7 +16,7 @@
 macros=r'''
 \newcommand{\Reals}{\mathbb{R}}
 \newcommand{\Expect}[0]{\mathbb{E}}
-\newcommand{\NormDist}{\mathcal{N}}
+\newcommand{\NormDist}{\mathscr{N}}
 
 \newcommand{\DynMod}[0]{\mathscr{M}}
 \newcommand{\ObsMod}[0]{\mathscr{H}}
diff --git a/notebooks/scripts/T1 - DA & EnKF.py b/notebooks/scripts/T1 - DA & EnKF.py
index 9e0f321..921552d 100644
--- a/notebooks/scripts/T1 - DA & EnKF.py	
+++ b/notebooks/scripts/T1 - DA & EnKF.py	
@@ -17,7 +17,7 @@
 # *Copyright (c) 2020, Patrick N. Raanes
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # ### Jupyter
diff --git a/notebooks/scripts/T2 - Gaussian distribution.py b/notebooks/scripts/T2 - Gaussian distribution.py
index 8688df7..019a21c 100644
--- a/notebooks/scripts/T2 - Gaussian distribution.py	
+++ b/notebooks/scripts/T2 - Gaussian distribution.py	
@@ -30,7 +30,7 @@
 # # T2 - The Gaussian (Normal) distribution
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 # Computers generally represent functions *numerically* by their values on a grid
 # of points (nodes), an approach called ***discretisation***.
@@ -43,14 +43,14 @@
 
 
 # ## The univariate (a.k.a. 1-dimensional, scalar) case
-# Consider the Gaussian random variable $x \sim \mathcal{N}(\mu, \sigma^2)$.  
+# Consider the Gaussian random variable $x \sim \NormDist(\mu, \sigma^2)$.  
 # Its probability density function (**pdf**),
 # $
-# p(x) = \mathcal{N}(x \mid \mu, \sigma^2)
+# p(x) = \NormDist(x \mid \mu, \sigma^2)
 # $ for $x \in (-\infty, +\infty)$,
 # is given by
 # $$\begin{align}
-# \mathcal{N}(x \mid \mu, \sigma^2) = (2 \pi \sigma^2)^{-1/2} e^{-(x-\mu)^2/2 \sigma^2} \,. \tag{G1}
+# \NormDist(x \mid \mu, \sigma^2) = (2 \pi \sigma^2)^{-1/2} e^{-(x-\mu)^2/2 \sigma^2} \,. \tag{G1}
 # \end{align}$$
 #
 # Run the cell below to define a function to compute the pdf (G1) using the `scipy` library.
@@ -89,7 +89,7 @@ def plot_pdf(mu=0, sigma=5):
 # show_answer('pdf_G1')
 # -
 
-# **Exc -- Derivatives:** Recall $p(x) = \mathcal{N}(x \mid \mu, \sigma^2)$ from eqn (G1).  
+# **Exc -- Derivatives:** Recall $p(x) = \NormDist(x \mid \mu, \sigma^2)$ from eqn (G1).  
 # Use pen, paper, and calculus to answer the following questions,  
 # which derive some helpful mnemonics about the distribution.
 #
@@ -115,7 +115,7 @@ def plot_pdf(mu=0, sigma=5):
 # -
 
 # #### Exc (optional) -- Integrals
-# Recall $p(x) = \mathcal{N}(x \mid \mu, \sigma^2)$ from eqn (G1). Abbreviate it using $c = (2 \pi \sigma^2)^{-1/2}$.  
+# Recall $p(x) = \NormDist(x \mid \mu, \sigma^2)$ from eqn (G1). Abbreviate it using $c = (2 \pi \sigma^2)^{-1/2}$.  
 # Use pen, paper, and calculus to show that
 #  - (i) the first parameter, $\mu$, indicates its **mean**, i.e. that $$\mu = \Expect[x] \,.$$
 #    *Hint: you can rely on the result of (iii)*
diff --git a/notebooks/scripts/T3 - Bayesian inference.py b/notebooks/scripts/T3 - Bayesian inference.py
index 3bd9206..ea2bf3e 100644
--- a/notebooks/scripts/T3 - Bayesian inference.py	
+++ b/notebooks/scripts/T3 - Bayesian inference.py	
@@ -27,14 +27,14 @@
 # Now that we have reviewed some probability, we can look at statistical inference.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # The [previous tutorial](T2%20-%20Gaussian%20distribution.ipynb)
 # studied the Gaussian probability density function (pdf), defined by:
 #
 # $$\begin{align}
-# \mathcal{N}(x \mid \mu, \sigma^2) &= (2 \pi \sigma^2)^{-1/2} e^{-(x-\mu)^2/2 \sigma^2} \,,\tag{G1} \\
+# \NormDist(x \mid \mu, \sigma^2) &= (2 \pi \sigma^2)^{-1/2} e^{-(x-\mu)^2/2 \sigma^2} \,,\tag{G1} \\
 # \NormDist(\x \mid  \mathbf{\mu}, \mathbf{\Sigma})
 # &=
 # |2 \pi \mathbf{\Sigma}|^{-1/2} \, \exp\Big(-\frac{1}{2}\|\x-\mathbf{\mu}\|^2_\mathbf{\Sigma} \Big) \,, \tag{GM}
@@ -299,12 +299,12 @@ def Bayes2(  corr_R =.6,                 y1=1,          R1=4**2,
 # In response to this computational difficulty, we try to be smart and do something more analytical ("pen-and-paper"): we only compute the parameters (mean and (co)variance) of the posterior pdf.
 #
 # This is doable and quite simple in the Gaussian-Gaussian case, when $\ObsMod$ is linear (i.e. just a number):  
-# - Given the prior of $p(x) = \mathcal{N}(x \mid x\supf, P\supf)$
-# - and a likelihood $p(y|x) = \mathcal{N}(y \mid \ObsMod x,R)$,  
+# - Given the prior of $p(x) = \NormDist(x \mid x\supf, P\supf)$
+# - and a likelihood $p(y|x) = \NormDist(y \mid \ObsMod x,R)$,  
 # - $\implies$ posterior
 # $
 # p(x|y)
-# = \mathcal{N}(x \mid x\supa, P\supa) \,,
+# = \NormDist(x \mid x\supa, P\supa) \,,
 # $
 # where, in the 1-dimensional/univariate/scalar (multivariate is discussed in [T5](T5%20-%20Multivariate%20Kalman%20filter.ipynb)) case:
 #
@@ -324,7 +324,7 @@ def Bayes2(  corr_R =.6,                 y1=1,          R1=4**2,
 # - (a) Actually derive the first term of the RHS, i.e. eqns. (5) and (6).  
 #   *Hint: you can simplify the task by first "hiding" $\ObsMod$ by astutely multiplying by $1$ somewhere.*
 # - (b) *Optional*: Derive the full RHS (i.e. also the second term).
-# - (c) Derive $p(x|y) = \mathcal{N}(x \mid x\supa, P\supa)$ from eqns. (5) and (6)
+# - (c) Derive $p(x|y) = \NormDist(x \mid x\supa, P\supa)$ from eqns. (5) and (6)
 #   using part (a), Bayes' rule (BR2), and the Gaussian pdf (G1).
 
 # +
@@ -333,11 +333,11 @@ def Bayes2(  corr_R =.6,                 y1=1,          R1=4**2,
 
 # **Exc -- Temperature example:**
 # The statement $x = \mu \pm \sigma$ is *sometimes* used
-# as a shorthand for $p(x) = \mathcal{N}(x \mid \mu, \sigma^2)$. Suppose
+# as a shorthand for $p(x) = \NormDist(x \mid \mu, \sigma^2)$. Suppose
 # - you think the temperature $x = 20°C \pm 2°C$,
 # - a thermometer yields the observation $y = 18°C \pm 2°C$.
 #
-# Show that your posterior is $p(x|y) = \mathcal{N}(x \mid 19, 2)$
+# Show that your posterior is $p(x|y) = \NormDist(x \mid 19, 2)$
 
 # +
 # show_answer('GG BR example')
diff --git a/notebooks/scripts/T4 - Time series filtering.py b/notebooks/scripts/T4 - Time series filtering.py
index 15b4546..5fc0f57 100644
--- a/notebooks/scripts/T4 - Time series filtering.py	
+++ b/notebooks/scripts/T4 - Time series filtering.py	
@@ -28,7 +28,7 @@
 # let's get more familiar with time-dependent (temporal/sequential) problems.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # ## Example problem: AR(1)
@@ -153,7 +153,7 @@ def exprmt(seed=4, nTime=50, M=0.97, logR=1, logQ=1, analyses_only=False, logR_b
 # Formulae (5) and (6) are called the **forecast step** of the KF.
 # But when $y_1$ becomes available, according to eqn. (Obs),
 # then we can update/condition our estimate of $x_1$, i.e. compute the posterior,
-# $p(x_1 | y_1) = \mathcal{N}(x_1 \mid x\supa_1, P\supa_1) \,,$
+# $p(x_1 | y_1) = \NormDist(x_1 \mid x\supa_1, P\supa_1) \,,$
 # using the formulae we developed for Bayes' rule with
 # [Gaussian distributions](T3%20-%20Bayesian%20inference.ipynb#Gaussian-Gaussian-Bayes'-rule-(1D)).
 #
diff --git a/notebooks/scripts/T5 - Multivariate Kalman filter.py b/notebooks/scripts/T5 - Multivariate Kalman filter.py
index eb8297b..3005708 100644
--- a/notebooks/scripts/T5 - Multivariate Kalman filter.py	
+++ b/notebooks/scripts/T5 - Multivariate Kalman filter.py	
@@ -29,7 +29,7 @@
 # Dealing with vectors and matrices is a lot like plain numbers. But some things get more complicated.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # ## Another time series problem, now multivariate
diff --git a/notebooks/scripts/T6 - Geostats & Kriging (optional).py b/notebooks/scripts/T6 - Geostats & Kriging (optional).py
index be4f2c4..dd88888 100644
--- a/notebooks/scripts/T6 - Geostats & Kriging (optional).py	
+++ b/notebooks/scripts/T6 - Geostats & Kriging (optional).py	
@@ -36,7 +36,7 @@
 # and their estimation.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # Set some parameters
diff --git a/notebooks/scripts/T7 - Chaos & Lorenz (optional).py b/notebooks/scripts/T7 - Chaos & Lorenz (optional).py
index 052c29f..fde6b37 100644
--- a/notebooks/scripts/T7 - Chaos & Lorenz (optional).py	
+++ b/notebooks/scripts/T7 - Chaos & Lorenz (optional).py	
@@ -29,7 +29,7 @@
 # As opposed to the opinions of Descartes/Newton/Laplace, chaos effectively means that even in a deterministic (non-stochastic) universe, we can only predict "so far" into the future. This will be illustrated below using two toy-model dynamical systems made by ***Edward Lorenz***.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # ## Dynamical systems
diff --git a/notebooks/scripts/T8 - Monte-Carlo & ensembles.py b/notebooks/scripts/T8 - Monte-Carlo & ensembles.py
index 89517de..014bc4f 100644
--- a/notebooks/scripts/T8 - Monte-Carlo & ensembles.py	
+++ b/notebooks/scripts/T8 - Monte-Carlo & ensembles.py	
@@ -35,7 +35,7 @@
 # An ensemble is an *iid* sample. I.e. a set of "members" ("particles", "realizations", or "sample points") that have been drawn ("sampled") independently from the same distribution. With the EnKF, these assumptions are generally tenuous, but pragmatic.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 #
 # Ensembles can be used to characterize uncertainty: either by using it to compute (estimate) *statistics* thereof, such as the mean, median, variance, covariance, skewness, confidence intervals, etc (any function of the ensemble can be seen as a "statistic"), or by using it to reconstruct the distribution/density from which it is sampled. The latter is illustrated by the plot below. Take a moment to digest its code, and then answer the following exercises.
@@ -77,7 +77,7 @@ def pdf_reconstructions(seed=5,       nbins=10,      bw=.3):
 #
 # **Exc -- Multivariate Gaussian sampling:**
 # Suppose $\z$ is a standard Gaussian,
-# i.e. $p(\z) = \mathcal{N}(\z \mid \bvec{0},\I_{\xDim})$,
+# i.e. $p(\z) = \NormDist(\z \mid \bvec{0},\I_{\xDim})$,
 # where $\I_{\xDim}$ is the $\xDim$-dimensional identity matrix.  
 # Let $\x = \mat{L}\z + \mu$.
 #
diff --git a/notebooks/scripts/T9 - Writing your own EnKF.py b/notebooks/scripts/T9 - Writing your own EnKF.py
index b6ab839..731146c 100644
--- a/notebooks/scripts/T9 - Writing your own EnKF.py	
+++ b/notebooks/scripts/T9 - Writing your own EnKF.py	
@@ -30,7 +30,7 @@
 # a forecast step and an analysis step.
 # $
 # % ######################################## Loading TeX (MathJax)... Please wait ########################################
-# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathcal{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
+# \newcommand{\Reals}{\mathbb{R}} \newcommand{\Expect}[0]{\mathbb{E}} \newcommand{\NormDist}{\mathscr{N}} \newcommand{\DynMod}[0]{\mathscr{M}} \newcommand{\ObsMod}[0]{\mathscr{H}} \newcommand{\mat}[1]{{\mathbf{{#1}}}} \newcommand{\bvec}[1]{{\mathbf{#1}}} \newcommand{\trsign}{{\mathsf{T}}} \newcommand{\tr}{^{\trsign}} \newcommand{\ceq}[0]{\mathrel{≔}} \newcommand{\xDim}[0]{D} \newcommand{\supa}[0]{^\text{a}} \newcommand{\supf}[0]{^\text{f}} \newcommand{\I}[0]{\mat{I}} \newcommand{\K}[0]{\mat{K}} \newcommand{\bP}[0]{\mat{P}} \newcommand{\bH}[0]{\mat{H}} \newcommand{\bF}[0]{\mat{F}} \newcommand{\R}[0]{\mat{R}} \newcommand{\Q}[0]{\mat{Q}} \newcommand{\B}[0]{\mat{B}} \newcommand{\C}[0]{\mat{C}} \newcommand{\Ri}[0]{\R^{-1}} \newcommand{\Bi}[0]{\B^{-1}} \newcommand{\X}[0]{\mat{X}} \newcommand{\A}[0]{\mat{A}} \newcommand{\Y}[0]{\mat{Y}} \newcommand{\E}[0]{\mat{E}} \newcommand{\U}[0]{\mat{U}} \newcommand{\V}[0]{\mat{V}} \newcommand{\x}[0]{\bvec{x}} \newcommand{\y}[0]{\bvec{y}} \newcommand{\z}[0]{\bvec{z}} \newcommand{\q}[0]{\bvec{q}} \newcommand{\br}[0]{\bvec{r}} \newcommand{\bb}[0]{\bvec{b}} \newcommand{\bx}[0]{\bvec{\bar{x}}} \newcommand{\by}[0]{\bvec{\bar{y}}} \newcommand{\barB}[0]{\mat{\bar{B}}} \newcommand{\barP}[0]{\mat{\bar{P}}} \newcommand{\barC}[0]{\mat{\bar{C}}} \newcommand{\barK}[0]{\mat{\bar{K}}} \newcommand{\D}[0]{\mat{D}} \newcommand{\Dobs}[0]{\mat{D}_{\text{obs}}} \newcommand{\Dmod}[0]{\mat{D}_{\text{obs}}} \newcommand{\ones}[0]{\bvec{1}} \newcommand{\AN}[0]{\big( \I_N - \ones \ones\tr / N \big)}
 # $
 
 # This presentation follows the traditional template, presenting the EnKF as the "the Monte Carlo version of the KF