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Markov Chain Monte Carlo

Sampling methods for approximating distributions, especially for Bayesian inference in complex models.

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  1. Bayesian Inference ✓ understood

    Using Bayes' theorem to update beliefs about parameters given data, incorporating uncertainty.

  2. Markov Chain Monte Carlo · you are here ✓ understood

Sampling methods for approximating distributions, especially for Bayesian inference in complex models.

This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.

  • Bayesian Inference
  • Sampling
  • Gibbs Sampling

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Bayesian Inference
Markov Chain Monte Carlo

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Gibbs Sampling

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Foundations Gibbs Sampling An MCMC method that samples from conditional distributions to approximate joint distributions. Foundations Variational Inference Approximating complex distributions by optimizing over a simpler family, an alternative to MCMC. Foundations Posterior Distribution In Bayesian methods, the updated belief about parameters after observing data. Agents & RL Monte Carlo Tree Search A search algorithm combining tree search with random sampling, used in game-playing AIs.