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AI & MLCoreAIML-PGM-008

Monte Carlo Estimation and Importance Sampling

Estimating expectations by sampling, and reweighting samples drawn from the wrong distribution.

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

Why it matters

The learner can estimate an expectation by Monte Carlo and explain how a poor proposal inflates importance-sampling variance.

The route to this topic

Route AIML-PGM-008
Natural Numbers What Is a Model? Variables & Constants Tasks: Regression, Classification, and Beyond Solving Linear Equations Function Notation & Composition Probabilistic Prediction Complex Numbers Basic Ratios Unit Circle 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 Random Variables & Distributions Expectation & Variance Law of Large Numbers Monte Carlo Estimation and Importance Sampling