Question: capture and recapture with hypergeometric

[jessegrabowski]

@OriolAbril

This is not exactly a posterior predictive check. First it is plotting a latent variable, so it can’t be compared to any observation, second it is comparing the “mean” type line with a histogram of all the samples from all draws and chains to random subsets of multiple samples from multiple chains and draws, not all of them but multiple. The base idea of a posterior predictive check is comparing the observed data to in sample posterior predictive samples. If the model is correct, one should not be able to distinguish the observed data from the posterior predictive samples. By in sample posterior predictive samples I mean posterior predictive samples that correspond to predictions on the exact same data that was used to fit the model (i.e. in a regression, use the same x instead of changing it to generate actual predictions/out of sample posterior predictive samples).

Thank you for this clarification! I wrongly assumed that the PPC was doing a type of simple bootstrapping from the posterior over the observed variable, and extended that logic to the latent variables. I didn’t notice that pm.sample_posterior_predictive draws samples for each observation, so you get back n_obs x n_chains x n_draws.