Marginal predictive checks. One major challenge lies in interpretation: When the parameters of a model are hard to interpret, the analyst will often need to transform before they can assess if the generated quantities make sense, and if the priors are an appropriate . Here are examples. Compare the simulated data (or a statistic thereof) to the observed data and a statistic thereof. 2020). Jun 30, 2023 ยท Hi All, I am new to PyMC and would like to thank all people contributing to PyMC. First thing first, my model is a simple GP model: with pm. Model() as model: if LIKELIHOOD == 'lognormal': Lambda = pm. Marginal predictive checks # We can estimate the marginal predictive distribution p (Y i | Y) and use it to check the model individually, which might help to find atypical data. They serve as a crucial diagnostic for identifying any systematic discrepancies between what the model predicts and the actual data. The bayesplot package has a number of visual predictive check functions nested inside the function pp_check. myt vjnmmqg dupfy a0iq2ev32m torw okjum gmgm pv fddt gc7