This chapter covers bayesian inference.

Upon completion of this chapter, students will be able to:

• Understand the fundamental concepts

• Apply computational techniques

• Implement methods in Python

Sections

• Bayesian Philosophy
  • Introduction
  • Key Concepts
  • Mathematical Framework
  • Python Implementation
  • Examples
  • Summary

prior_distributions likelihood_posterior bayesian_updating credible_intervals multiparameter_models numerical_integration mcmc_introduction metropolis_hastings gibbs_sampling convergence_diagnostics posterior_predictive_checks model_comparison