Markov Chain Monte Carlo Courses and Certifications

Richard McElreath

Statistical Rethinking 2023 - Markov Chain Monte Carlo

Explore Markov Chain Monte Carlo methods, from basic concepts to advanced techniques like Hamiltonian Monte Carlo, with practical applications and diagnostics for statistical modeling.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Richard McElreath

Statistical Rethinking - Markov Chain Monte Carlo

Explore Markov chain Monte Carlo and Hamiltonian Monte Carlo methods, including practical implementation in Stan and diagnostic techniques for statistical modeling.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Simons Institute

Sampling Crash Course

Explore geometric methods in optimization and sampling, covering direct methods, Markov chain Monte Carlo, and probabilistic approaches for efficient algorithm design and analysis.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

International Mathematical Union

Enhancing Markov Chain Monte Carlo Sampling Methods with Deep Learning

Explore advanced techniques combining Markov Chain Monte Carlo sampling with deep learning, focusing on importance sampling, normalizing flows, and non-equilibrium methods for enhanced efficiency in high-dimensional problems.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Alan Turing Institute

Stratification for Markov Chain Monte Carlo

Explore advanced Markov Chain Monte Carlo techniques, focusing on stratification methods for complex systems modeling and statistical inference in various scientific domains.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Mathematical Foundation of Various MCMC Methods

Explore advanced MCMC methods for combinatorial optimization, including multi-spin flip techniques. Compare mathematical and numerical results to evaluate efficiency in different contexts.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Centre de recherches mathématiques - CRM

An Exploration-Agnostic Characterization of the Ergodicity of Parallel Tempering

Explore non-reversible parallel tempering's ergodicity in complex distributions, examining its efficiency and comparing it to classical methods through theoretical analysis and simulations.

• YouTube
• 48 minutes
• On-Demand
• Free Video

MICDE University of Michigan

Multiscale Generalized Hamiltonian Monte Carlo with Delayed Rejection

Explore advanced sampling techniques combining generalized Hamiltonian Monte Carlo and delayed rejection for efficient multiscale distribution sampling, overcoming limitations of standard HMC methods.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Sampling through Exploration Exploitation in Markov Chain Monte Carlo

Explore an innovative Markov chain Monte Carlo algorithm combining global proposals and mobile moves for efficient, parallelizable sampling in various applications.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Probabilistic Graphical Models 2: Inference

485 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
• On-Demand
• Free Online Course (Audit)

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
• On-Demand
• Free Online Course (Audit)

University of Tübingen

Probabilistic Machine Learning

Comprehensive lecture series on probabilistic machine learning, covering Bayesian paradigms, statistical concepts, and deep learning connections for master's-level computer science students.

• YouTube
• 1 day 16 hours
• On-Demand
• Free Online Course

Mathematical understanding of uncertainty

Explore probability theory, universal principles, and Markov chains to understand and harness uncertainty in real-world applications, from statistics to deep learning algorithms.

• edX
• 12 weeks, 1-2 hours a week
• On-Demand
• Free Online Course (Audit)

Harvard CMSA

Advances in Distribution Compression - From Kernel Thinning to Stein Thinning

Discover advanced techniques for probability distribution compression, focusing on kernel and Stein thinning methods for efficient computational modeling and simulation optimization.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Harvard CMSA

Revisiting Jerrum's Metropolis Process for the Planted Clique Problem

Delve into advanced graph theory analysis examining Metropolis process limitations in planted clique recovery, focusing on algorithmic thresholds and natural initialization challenges.

• YouTube
• 50 minutes
• On-Demand
• Free Video