bayesian-methods

Bayes factors allow you to compare models across experiments and study designs by quantifying evidence. This Skill provides reproducible workflows to calculate these factors and compare statistical models using Python libraries.

Specifying priors for multi-parameter Bayesian models requires selecting appropriate distributions based on prior knowledge. This Skill provides guidance for prior specification in single- and multi-parameter models to ensure principled data analysis.

Posterior interpretation involves analyzing credible intervals and running diagnostic checks to validate model fit. This Skill provides aids for interpreting posterior distributions, calculating credible intervals, and performing necessary diagnostic checks.

Bayesian data analysis supports robust inference across various experiments and study designs by incorporating prior knowledge. This Skill enables you to apply these statistical methods to derive posterior beliefs and compare models effectively.

Generating posterior samples involves computing the posterior distribution from specified priors and observed data. This Skill supports generating posterior samples and evaluating models within reproducible Python workflows.

Bayesian methods are preferred when you need to incorporate prior knowledge and compare models using Bayes factors rather than p-values. This framework provides a principled approach for deriving posterior beliefs and robust inference in data analysis.