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AI & MLRecommendedAIML-PGM-009

Markov Chain Monte Carlo

Sampling hard distributions by designing a Markov chain whose stationary distribution is the target.

Difficulty
4 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can run Metropolis-Hastings on a 2D target and diagnose poor mixing from the trace and the proposal scale.

The route to this topic

Route AIML-PGM-009
Natural Numbers What Is a Model? Variables & Constants Tasks: Regression, Classification, and Beyond Solving Linear Equations Function Notation & Composition Probabilistic Prediction Complex Numbers Matrices Intro Basic Ratios Unit Circle Matrix Algebra Polynomial Functions Intuitive Limits Continuity Definition of the Derivative Differentiation Rules Chain Rule Antiderivatives & Indefinite Integrals Derivatives of Transcendental Functions Riemann Sums & Definite Integral Fundamental Theorem of Calculus Integration by Parts Sample Spaces & Events Conditional Probability & Independence Random Variables & Distributions Expectation & Variance Markov Chains Law of Large Numbers Monte Carlo Estimation and Importance Sampling Markov Chain Monte Carlo