Markov chain Monte Carlo (MCMC): Difference between revisions
Editor iHTS (talk | contribs) (Created page with "A probabilistic analysis method combined with the Markov model, which captures the transitional probabilities between various states to study the interaction between the probability distributions attached to each variable. '''Note:''' This type of simulation provides an estimate of the degree of uncertainty in a model. It is widely used in Bayesian analysis to build or update models of the joint distribution of variables. [http://htaglossary.net/Markov-chain-Monte-Carl...") |
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'''Note:''' This type of simulation provides an estimate of the degree of uncertainty in a model. It is widely used in Bayesian analysis to build or update models of the joint distribution of variables. | '''Note:''' This type of simulation provides an estimate of the degree of uncertainty in a model. It is widely used in Bayesian analysis to build or update models of the joint distribution of variables. | ||
== Reference == | |||
http://htaglossary.net/Markov-chain-Monte-Carlo-(MCMC) | |||
Latest revision as of 13:44, 21 November 2023
A probabilistic analysis method combined with the Markov model, which captures the transitional probabilities between various states to study the interaction between the probability distributions attached to each variable.
Note: This type of simulation provides an estimate of the degree of uncertainty in a model. It is widely used in Bayesian analysis to build or update models of the joint distribution of variables.