pymc-bayesian-modeling

You can quantify uncertainty in predictive modeling by applying Bayesian inference with PyMC, which supports priors selection, NUTS sampling, and posterior predictive checks to provide principled uncertainty estimates for your data-driven decisions.

Build and fit a hierarchical model with PyMC by defining multilevel priors, drawing posterior samples using NUTS or variational inference, and validating results using posterior predictive checks and ArviZ diagnostics.

Yes, you can compare Bayesian models using LOO and WAIC criteria to evaluate predictive accuracy, running model comparison alongside posterior predictive checks to select the best fitting architecture for your data.

Yes, the Bayesian workflow supports both MCMC NUTS sampling and variational inference, allowing you to fit linear, logistic, and hierarchical models and extract posterior distributions for forecasting and analytics.

Run posterior predictive checks after MCMC sampling by generating replicated data from your fitted PyMC model and analyzing the distributions with ArviZ to validate that your model accurately captures the observed data patterns.

Prepare data using pandas and numpy arrays, then select appropriate priors for your Bayesian logistic model in PyMC, ensuring your probability distributions accurately reflect domain knowledge before initiating NUTS sampling.