Markov Chain Monte Carlo Courses and Certifications

Richard McElreath

Statistical Rethinking - Markov Chain Monte Carlo

Explore Markov chain Monte Carlo through Metropolis and Hamiltonian Monte Carlo, with Stan implementation and diagnostics for identifying bad chains.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2023 - Markov Chain Monte Carlo

Explore Markov chain Monte Carlo, Hamiltonian Monte Carlo, diagnostics, and applications to wine data, judges, and item response theory.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2026 - MCMC and Item Response Models - Lecture A08

Explore MCMC techniques and item response models for advanced statistical analysis and Bayesian inference applications.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

The National Centre for Research Methods

Advanced Bayesian Methods

An advanced introduction to Bayesian computation, covering Metropolis-Hastings, Gibbs sampling, computational speed, and convergence assessment.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

Probabilistic Graphical Models 2: Inference

491 ratings at Coursera

Answer queries with probabilistic graphical models: variable elimination, clique tree belief propagation, MAP message passing, and sampling methods like Gibbs and Metropolis-Hastings.

• Coursera
• 7 weeks, 5 hours a week
• Self-Paced
• Paid Course

Bayesian Statistics: Techniques and Models

497 ratings at Coursera

Build, fit, and compare Bayesian models in R and JAGS: MCMC with Metropolis-Hastings and Gibbs sampling, linear regression, ANOVA, logistic and Poisson regression.

• Coursera
• 5 weeks, 6 hours a week
• Self-Paced
• Paid Course

Bayesian Statistical Concepts and Methods

Apply Bayesian methods to data analysis: work with posterior and posterior predictive distributions, directed acyclic graphs, Markov chain Monte Carlo, and hierarchical models using R and Stan.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Discrete-Time Markov Chains and Monte Carlo Methods

20 ratings at Coursera

Compute transition matrices, limiting and stationary distributions, and first-step analyses for Markov chains, then simulate with accept-reject, Metropolis-Hastings, Gibbs sampling, and Markov decision processes.

• Coursera
• 6 weeks, 5 hours a week
• Self-Paced
• Paid Course

Computational Bayesian Statistics for Data Science

Approximate posteriors when algebra fails: compute MAP estimates by gradient ascent, run rejection sampling, Gibbs and Metropolis-Hastings MCMC, and fit Bayesian models with Stan in R.

• Coursera
• 5 weeks, 5 hours a week
• Self-Paced
• Paid Course

EDUCBA

Bayesian Statistics: Excel to Python A/B Testing

27 ratings at Coursera

Apply Bayesian inference to A/B testing and healthcare analytics: run MCMC sampling with PyMC, fit hierarchical models, and build Bayesian probability tables in Excel.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Bayesian Inference Fundamentals

Apply Bayes' theorem to random variables, use conjugate priors, derive credible intervals, and implement Gibbs sampling and Metropolis-Hastings with convergence diagnostics.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

Bayesian Computational Statistics

Learn Bayesian inference from prior and posterior distributions to hierarchical models, MCMC and simulation methods, and Bayesian regression, implemented in R or Python.

• Coursera
• 9 weeks, 10 hours a week
• Self-Paced
• Paid Course

Validation of Safety Critical Systems

Master mathematical validation techniques for autonomous systems using sampling methods, formal verification, and reachability analysis in safety-critical applications.

• YouTube
• 18 hours 50 minutes
• Self-Paced
• Free Video

Applied Bayesian for Analytics

Learn how to construct, fit, estimate and compute Bayesian statistical models with the help of OpenBUGS (freely available software)

• edX
• 6 weeks, 2-3 hours a week
• Self-Paced
• Free Certificate

Introduction to Bayesian Statistics Using R

10 ratings at edX

Learn the fundamentals of Bayesian approach to data analysis, and practice answering real life questions using R.

• edX
• 6 weeks, 5-10 hours a week
• Self-Paced
• Free Online Course (Audit)