Posterior predictive checks The toolbox now calculates 2 measures of “goodness of fit” of the models. This is a useful quantitative reassurance that the models describe the participant discounting behaviour better than chance. In turn, this is important when we come to deciding which (if any) data files we should exclude. You can go and […]
Vincent, B. T. (2015) A tutorial on Bayesian models of perception, Journal of Mathematical Psychology, 66:103–114.
I’ve just released a small bit of Matlab code on GitHub which helps automate the job of plotting posterior predictive distributions. If you are inferring posterior distributions of parameters of a 1D function (e.g. y=mx+c) then this code will plot the posterior predictive distribution for you. This should be handy for you to eyeball how well a model […]
If you’ve decided to join the increasing number of people using MCMC methods to conduct Bayesian inference, then one important decision is which software to use. This decision will be influenced by your programming language of choice, see Figure below. If you use Matlab, then really your best choice at the moment is JAGS. You use it […]