What Are Common Bayesian Model Diagnostic Tools?

Описание: Dive into the essential diagnostic tools used to assess the reliability and validity of your Bayesian models. Understanding these methods is crucial for ensuring your statistical inferences are sound and trustworthy. This video explores the common techniques statisticians use to check if their models are performing as expected.

In this video, you'll learn about key diagnostic tools for Bayesian modeling, including:
► Convergence diagnostics, such as R-hat and effective sample size, to ensure your Markov Chain Monte Carlo (MCMC) sampler has converged.
► Trace plots and auto-correlation plots, which visually help identify issues like poor mixing or high correlation within chains.
► Posterior predictive checks (PPC) for assessing how well your model predicts new data and identifying potential model misspecification.
► The importance of checking for divergent transitions, especially in Hamiltonian Monte Carlo, which can indicate problematic posterior geometry.
► Sensitivity analysis to understand how robust your model's conclusions are to changes in prior specifications.


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