Bayesian methods
Markov Chain Monte Carlo (MCMC)
Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
- Brooks, S., Gelman, A., Jones, G. & Meng, X.-L. (Eds.). (2011). Handbook of Markov Chain Monte Carlo. CRC Press. ISBN: 978-1420079418
Related methods
Referenced by
Automatic Differentiation Variational InferenceBayes Factor TestBayesian ANOVABayesian Factor AnalysisBayesian Hierarchical ModelBayesian Inference with Measurement ErrorBayesian Linear RegressionBayesian Logistic RegressionBayesian Model AveragingBayesian Model Averaging with Measurement ErrorBayesian NetworkBayesian Nonparametric MethodsBayesian RegressionBayesian SEMBayesian Structural Time SeriesConjugate Prior AnalysisDirichlet Process Mixture ModelEmpirical BayesExpectation PropagationGibbs SamplingHamiltonian Monte CarloHierarchical Bayesian InferenceHierarchical Hamiltonian Monte CarloHierarchical Variational InferenceLaplace ApproximationMCMC for Model ComparisonMCMC with Measurement ErrorMultilevel Bayesian InferenceNo-U-Turn SamplerParticle FilterRobust Bayesian Model AveragingRobust Markov chain Monte CarloSequential Monte CarloSlice SamplingVariational Inference