Markov Chains and Monte Carlo Methods

Markov Chains and Monte Carlo Methods is a research topic within Statistics and Probability. Science Explorer counts 12k research works in it since 1950. 10.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of Bayesian Monte Carlo methods, such as Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation, and Hamiltonian Monte Carlo, in scientific inference for inverse problems, model selection, and statistical estimation. It also explores adaptive MCMC algorithms and stochastic gradient Langevin dynamics for efficient parameter inference and approximation algorithms.

• Bayesian Monte Carlo
• Markov Chain
• Approximate Bayesian Computation
• Adaptive MCMC
• Hamiltonian Monte Carlo
• Stochastic Gradient Langevin Dynamics
• Inverse Problems
• Model Selection
• Statistical Inference
• Approximation Algorithms