Markov Chain Monte Carlo Courses and Certifications

Probabilistic Graphical Models 2: Inference

491 ratings at Coursera

Explore probabilistic inference in graphical models, covering exact and approximate algorithms for conditional probability queries and MAP inference, with hands-on programming assignments.

• Coursera
• 38 hours
• Self-Paced
• Paid Course

Institute for Pure & Applied Mathematics (IPAM)

Quantum Markov Chain Monte Carlo Algorithm - IPAM at UCLA

Explore quantum Markov Chain Monte Carlo algorithms for sampling Gibbs states, featuring a novel continuous-time quantum Markov chain with efficient simulation and practical applications in lattice Hamiltonians.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Bayesian Statistics: Techniques and Models

497 ratings at Coursera

Expand your Bayesian toolbox with advanced models and MCMC methods. Learn to construct, fit, assess, and compare sophisticated statistical models using R and JAGS for real-world data analysis.

• Coursera
• 1 day 5 hours 37 minutes
• Self-Paced
• Paid Course

Bayesian Statistical Concepts and Methods

Master Bayesian methods for data analysis using posterior distributions, MCMC algorithms, and hierarchical models with R and Stan platform implementation.

• Coursera
• 5 hours 30 minutes
• Self-Paced
• Paid Course

Computational Bayesian Statistics for Data Science

Master Bayesian statistical methods with computational algorithms like MAP estimation, MCMC, Gibbs sampling, and Metropolis-Hastings, while gaining hands-on Stan experience for real-world data science.

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

EDUCBA

Bayesian Statistics: Excel to Python A/B Testing

27 ratings at Coursera

Master Bayesian statistics from Excel basics to Python A/B testing, covering MCMC sampling, hierarchical models, and healthcare decision-making with hands-on probabilistic modeling.

• Coursera
• 6 hours 24 minutes
• Self-Paced
• Paid Course

Bayesian Inference Fundamentals

Master Bayesian inference from Bayes' theorem to MCMC methods, covering conjugate priors, credible intervals, Gibbs sampling, and Variational Inference for data-driven decision-making across industries.

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

Career Certificate

Applied Bayesian Data Analysis

Master Bayesian statistical methods, from foundational inference and MCMC to hierarchical models and probabilistic programming, with real-world applications in healthcare, sports analytics, and business.

• Coursera
• Self-Paced
• Paid Course
• 3 courses

Advanced Bayesian Methods and Applications

Master advanced Bayesian inference with variational inference, decision theory, and non-parametric methods. Use PyMC3 for probabilistic programming and apply models to sports analytics and medicine.

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

Bayesian Computational Statistics

Rigorous introduction to Bayesian inference, covering theory, computation, and practical implementation using statistical software. Explores advanced topics and applications in data analysis.

• Coursera
• 3 days 16 hours 39 minutes
• Self-Paced
• Paid Course

Computational Data Science in Physics III

Dive into numerical simulations for physics research, from differential equations to lattice models, culminating in applications relevant to contemporary lattice QCD.

• MITx Online
• 7 weeks, 10-15 hours/week
• Self-Paced
• Free Online 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

3Blue1Brown

The Math of Phase Change - Deriving the Boltzmann Formula and Defining Temperature

Explore the mathematical foundations of phase transitions through Boltzmann formulas, temperature definitions, and liquid-vapor simulations with interactive modeling.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2022

Master Bayesian inference, multilevel modeling, and causal reasoning through practical statistical methods that avoid common pitfalls in data analysis and research design.

• YouTube
• 1 day 46 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking Fall 2017

Master Bayesian statistical methods and causal inference through comprehensive lectures accompanying the acclaimed "Statistical Rethinking" book by Richard McElreath.

• YouTube
• 19 hours 46 minutes
• Self-Paced
• Free Video