add some info on MCMCchains and Arviz.jl

EpiAware/docs/src/getting-started/faq.md

@@ -1,11 +1,52 @@
 # Frequently asked questions
 
-This page contains a list of frequently asked questions about the EpiAware package. If you have a question that is not answered here, please open a discussion on the GitHub repository.
+This page contains a list of frequently asked questions about the `EpiAware` package. If you have a question that is not answered here, please open a discussion on the GitHub repository.
 
 ```@contents
 Pages = ["lib/getting-started/faq.md"]
 ```
 
 ## Pluto scripts
 
-We use [`Pluto.jl`](https://plutojl.org/) scripts as part of our documentation and testing. The scripts are located in `docs/src/examples` and can be run using the `Pluto.jl` package. We recommend using the version of `Pluto` that is pinned in the `Project.toml` file defining the documentation environment. An entry point to running or developing this documentation is the `docs/pluto-scripts.sh` bash shell script. Run this from the root directory of this repository.
+We use [`Pluto.jl`](https://plutojl.org/) scripts as part of our documentation and testing. The scripts are located in `docs/src/examples` and can be run using the `Pluto.jl` package.
+We recommend using the version of `Pluto` that is pinned in the `Project.toml` file defining the documentation environment.
+An entry point to running or developing this documentation is the `docs/pluto-scripts.sh` bash shell script. Run this from the root directory of this repository.
+
+## Manipulating `EpiAware` model specifications
+
+### Specifying models which are components
+
+#### Modular model construction
+
+#### Remaking models
+
+## Working with `Turing.jl` models
+
+### Conditioning and fixing models
+
+### Tools for working with `MCMCChain` objects
+
+#### [`MCMCChain.jl`](https://turinglang.org/MCMCChains.jl/stable/)
+
+[`MCMCChain.jl`](https://turinglang.org/MCMCChains.jl/stable/) is the package from which `MCMCChains` is imported. It provides a number of useful functions for working with `MCMCChain` objects. These include functions for summarising, plotting, and manipulating chains. Below is a list of some of the most useful functions.
+
+- `plot`: Plots trace  and density plots for each parameter in the chain object.
+- `histogram`: Plots histograms for each parameter in the chain object by chain.
+- `get`: Accesses the values of a parameter/s in the chain object.
+- `DataFrames.DataFrame` converts a chain into a wide format `DataFrame`.
+- `describe`: Prints the summary statistics of the chain object.
+
+There are many more functions available in the `MCMCChain.jl` package. For a full list of functions, see the [documentation](https://turinglang.org/MCMCChains.jl/stable/).
+
+#### [`Arviz.jl`](https://julia.arviz.org/ArviZ/stable/)
+
+An alternative to `MCMCChain.jl` is the `ArviZ.jl` package. `ArviZ.jl` is a Julia meta-package for exploratory analysis of Bayesian models. It is part of the ArviZ project, which also includes a related Python package.
+
+`ArviZ.jl` uses a `InferenceData` object to store the results of a Bayesian analysis. This object can be created from a `MCMCChain` object using the `from_mcmcchains` function.
+The `InferenceData` object can then be used to create a range of plots and summaries of the model.
+This is particularly useful as it allows you to specify the indexes of your parameters (for example you could use dates for time parameters).
+
+In addition to this useful functionality `from_mcmcchains` can also be used to combine posterior predictions with prior predictions, prior information and the log likelihood of the model (see [here](https://julia.arviz.org/ArviZ/stable/quickstart/) for an example of this).
+This unlocks a range of useful diagnostics and plots that can be used to assess the model.
+
+There is a lot of functionality in `ArviZ.jl` and it is worth exploring the [documentation](https://julia.arviz.org/ArviZ/stable/) to see what is available.