pymc-bayesian-modeling

To build and validate Bayesian models using PyMC, you construct hierarchical, regression, or time series models, then fit them using MCMC sampling or variational inference, and validate the fit using prior/posterior predictive checks and diagnostic analysis.

The best way to compare probabilistic models in Bayesian inference is by using information criteria like LOO or WAIC, which this approach supports for evaluating and comparing the predictive accuracy of different fitted models.

Yes, you can perform uncertainty quantification for time series analysis with Bayesian modeling by constructing time series models and fitting them through MCMC sampling to capture and quantify predictive uncertainty.

You need MCMC sampling versus variational inference for hierarchical models when determining your inference method: MCMC provides robust sampling for complex posterior distributions, while variational inference offers faster approximation for large-scale probabilistic programming.

Yes, this Bayesian modeling approach supports advanced statistical workflows including both prior and posterior predictive checks, enabling you to validate model assumptions and assess the fit of your probabilistic models.