Physical Sciences → Mathematics → Statistics and Probability

Markov Chains and Monte Carlo Methods

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.

33.7K works worldwide433.6K citations

Bayesian Monte CarloMarkov ChainApproximate Bayesian ComputationAdaptive MCMCHamiltonian Monte CarloStochastic Gradient Langevin DynamicsInverse ProblemsModel SelectionStatistical InferenceApproximation Algorithms