pymc

Building a hierarchical Bayesian model involves structuring your data with named dimensions and using probabilistic programming to define priors and likelihoods. This Skill provides tools to construct these models and perform inference for data analysis.

Comparing Bayesian models using LOO or WAIC involves calculating information criteria to estimate out-of-sample predictive accuracy. This Skill supports model comparison and posterior predictive checks to help you select the best performing model.

MCMC sampling for probabilistic programming works by drawing samples from a posterior distribution to approximate complex mathematical integrals. This Skill supports NUTS sampling and variational inference to perform Bayesian inference on your models.

Yes, you can use this Skill for Bayesian inference on sales data with missing values. It provides guidance on data standardization and handling missing data, allowing you to model customer behavior and make informed decisions.

Yes, you need PyMC and ArviZ to perform Bayesian modeling and model comparison, along with numpy, pandas, and matplotlib. These dependencies are required to build models, run sampling, and analyze the resulting inference data.

Variational inference offers faster approximation than MCMC sampling but may underestimate posterior uncertainty. This Skill supports both methods, allowing you to choose NUTS for precise hierarchical modeling or variational inference for faster estimates.