Posterior/prior predictive checks in Bayesian modelling w/ Mark Andrews

PsyPag Simulation Summer School

Posterior predictive checks are used to evaluate model fits and model assiumptions in Bayesian (MCMC) based analyses. They involve drawing samples from the posterior distribution to general hypothetical data sets, and then comparing these data sets with the observed data to indentify discrepancies between the model&#x27;s predictions and the reality of the data. Posterior predictive checks are particularly useful in complex models where the assumptions of the model are not very easy to check. By contrast to posterior predictive checks, prior predictive checks are very useful to clarify the assumptions of choices of priors. Again, hypothetical data sets are generated and these make it clear what kinds of the data the prior distribution does and does not assume.

Pre-requisite knowledge:Experience with general data analysis in R. Some experience with Bayesian methods using MCMC in R too, e.g. brms/Stan.R packages to be installed in advancetidyverse, tidybayes, brms, rstanRelevant Linkshttps://www.jstor.org/stable/24306036?seq=1 https://link.springer.com/article/10.1007/s001800000038

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Posterior/prior predictive checks in Bayesian modelling w/ Mark Andrews

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Posterior/prior predictive checks in Bayesian modelling w/ Mark Andrews

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