Markov Decision Processes
Machine Learning
A class of algorithms for sampling from probability distributions based on constructing a Markov chain that converges to the desired distribution.
Detailed Explanation
Markov Chain Monte Carlo (MCMC) is a set of algorithms used in machine learning to generate samples from complex probability distributions. It constructs a Markov chain whose equilibrium distribution matches the target distribution, allowing for efficient approximation of integrals and Bayesian inference by exploring the distribution through successive probabilistic steps.
Use Cases
- •Estimating Bayesian model parameters by sampling from posterior distributions with complex, high-dimensional spaces.
Related Terms
Other terms in the Machine Learning category