pymc-bayesian-modeling

Bayesian model comparison using LOO or WAIC evaluates predictive accuracy by estimating out-of-sample error. You fit candidate models with MCMC sampling, then apply these criteria to select the model with the best expected predictive performance.

The best way to quantify uncertainty in a Bayesian regression model is by computing the full posterior distribution. You use MCMC sampling or variational inference to capture credible intervals for all estimated parameters and predictions.

Yes, you can perform variational inference and MCMC sampling for time series models. The framework supports constructing time series structures and fitting them using either NUTS sampling or variational inference algorithms.

Yes, you need pandas and numpy to run probabilistic programming models because they are required dependencies. They handle data manipulation and numerical operations before and during the Bayesian inference process.

Your Bayesian inference shows divergences during MCMC sampling when the sampler encounters regions of high curvature in the posterior. Diagnose this using built-in checks to inspect R-hat, effective sample sizes, and trace plots for validation.