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What is MCMC Methods Explanation about?

Markov Chain Monte Carlo (MCMC) methods are algorithms for sampling from complex probability distributions, commonly used in Bayesian inference and reinforcement learning. Key concepts include Monte Carlo methods for random sampling and Markov chains where each state depends on the previous one. Core algorithms include Metropolis, Metropolis-Hastings, and Gibbs Sampling, each with specific procedures for generating samples from target distributions.

Author

Praneet singh Lashkari

Language

EN