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About Markov Chain Monte Carlo: Sampling the Un-sampleable

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How to draw samples from a probability distribution you can only evaluate up to a constant — the workhorse of Bayesian computation. You will understand why a Markov chain with the right stationary distribution turns into a sampler, derive the Metropolis-Hastings acceptance rule from detailed balance, tune proposals and diagnose mixing, run Gibbs updates from full conditionals, judge convergence with trace plots, effective sample size and R-hat, and understand why Hamiltonian Monte Carlo and NUTS dominate modern practice, including what divergences tell you about difficult posterior geometry.

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What You'll Learn

Concepts:
Sampling from Unnormalized Densities Hamiltonian Monte Carlo Divergent Transitions R-hat Convergence Diagnostic Proposal Distribution Tuning Markov Chain Monte Carlo Detailed Balance Gibbs Sampling Monte Carlo Estimation Metropolis-Hastings Acceptance Ratio Metropolis-Hastings Algorithm Mixing in MCMC Effective Sample Size Ergodicity No-U-Turn Sampler (NUTS) Burn-in and Warmup

What you will learn

No introduction video available

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