Sampling Methods - Interactive Visualization

When a distribution is too complex to sample from directly, we use MCMC methods. Rejection sampling proposes from a simple distribution and accepts with probability p(x)/M·q(x). Importance sampling reweights proposals. Metropolis-Hastings uses a random walk that explores the target distribution. This visualization animates all three on the same target distribution, showing acceptance rates and how samples build up.

• Watch rejection sampling propose and accept/reject in real time
• See importance sampling weights shown as dot sizes
• Watch Metropolis-Hastings random walk explore the target
• Compare acceptance rates between methods
• See trace plots showing the chain of accepted samples over time

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.