Rekodi ya ushahidi wa mbinu

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.

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Markov Chain Monte Carlo

Rekodi ya mbinu ya kiajenda · bayesian / bayesian

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

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Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyVariational Inferencemachine-suggested · Relational suggestion, not evidence.