diff --git a/WorkSpace/AlexP/SSIT_BurstingGene_MCMC.m b/WorkSpace/AlexP/SSIT_BurstingGene_MCMC.m
index d596edf..9438084 100644
--- a/WorkSpace/AlexP/SSIT_BurstingGene_MCMC.m
+++ b/WorkSpace/AlexP/SSIT_BurstingGene_MCMC.m
@@ -38,7 +38,7 @@
     F1 = F1.loadData(dataFile, dataToFit);
 end
 
-function result = performMLEandMCMC(F1,logSpace,stepSize)
+function result = performMLEandMCMC(F1,logSpace,stepSize,chainLength)
 
     % MLE Fitting Options
     maxFitIter = 1000;
@@ -56,7 +56,7 @@
     F1.fittingOptions.logPrior = @(x)-sum((log10(x)-log10PriorMean(1:4)).^2./(2*log10PriorStd(1:4).^2));
 
     % Metropolis Hastings Properties
-    nSamplesMH = 1000; % Number of MH Samples to run
+    nSamplesMH = chainLength; % Number of MH Samples to run
     nThinMH = 2; % Thin rate for MH sampling
     nBurnMH = 100; % Number for MH burn in
 
@@ -136,36 +136,23 @@ function plotMHTraces(samples, paramNames)
     sgtitle('Metropolis-Hastings Trace Plots');
 end
 
-%% ChatGPT 4 Compute ESS
-function acf = autocorr(x, max_lag)
-    n = length(x);
-    x_mean = mean(x);
-    acf = zeros(1, max_lag + 1);
-
-    for lag = 0:max_lag
-        num = sum((x(1:n-lag) - x_mean) .* (x(1+lag:n) - x_mean));
-        den = sum((x - x_mean).^2);
-        acf(lag + 1) = num / den;
-    end
-end
-
-function act = integrated_act(x, max_lag)
-    acf_values = autocorr(x, max_lag);
-    act = 1 + 2 * sum(acf_values(2:end));
-end
-
-function ess = effective_sample_size(x)
-    n = length(x);
-    max_lag = min(1000, n - 1); % You can choose an appropriate lag based on your data
-    act = integrated_act(x, max_lag);
-    ess = n / act;
+function ess = effective_sample_size(mhResults)
+    ac = xcorr(mhResults.mhValue-mean(mhResults.mhValue),'normalized');
+    ac = ac(size(mhResults.mhValue,1):end);
+    % Uncomment following plot line to plot autocorrelation function,
+    %% BUT this will overwrite one of the trace plots.
+    % plot(ac,'LineWidth',3); hold on
+    N = size(mhResults.mhValue,1);
+    tau = 1+2*sum((ac(2:N/100)));
+    Neff = N/tau;
+    ess = Neff;
 end
 
 F1 = setupAndSolveModel();
 
 % Get computation time for MCMC
 tic
-MHResults = performMLEandMCMC(F1,true,0.1);
+MHResults = performMLEandMCMC(F1,true,0.1,3000);
 toc
 
 % Assume MHResults.mhSamples is a 1000x4 matrix
@@ -177,21 +164,8 @@ function plotMHTraces(samples, paramNames)
 % Call the function to plot the traces
 plotMHTraces(samples, paramNames);
 
-% Assume MHResults.mhsamples is your MCMC chain with size 1000x4
-mcmc_chain = samples;
-[num_iterations, num_parameters] = size(mcmc_chain);
-
-% Initialize the ESS vector
-ess_values = zeros(1, num_parameters);
-
-% Calculate ESS for each parameter
-for param = 1:num_parameters
-    ess_values(param) = effective_sample_size(mcmc_chain(:, param));
-end
-
-% Display the ESS values
-disp('Effective Sample Size (ESS) for each parameter:');
-disp(ess_values);
+ess = effective_sample_size(MHResults);
+ess
 
 % Compute FIM for subsampling of MH results.
     %J = floor(linspace(nSamplesMH/2,nSamplesMH,nFIMsamples));