MCMC Numerical Approximation Task: Difference between revisions

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=== 2015 ===
=== 2015 ===
* (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo#Application_domains Retrieved:2015-11-16.
* (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo#Application_domains Retrieved:2015-11-16.
** MCMC methods are primarily used for calculating [[Numerical analysis|numerical approximations]] of [[Multiple integral|multi-dimensional integrals]], for example in [[Bayesian statistics]], [[computational physics]], [[computational biology]] and [[computational linguistics]]. <ref> See Gill 2008. </ref> <ref> See Robert & Casella 2004. </ref> * In [[Bayesian statistics]], the recent development of MCMC methods has been a key step in making it possible to compute large [[Bayesian network#Hierarchical models|hierarchical models]] that require integrations over hundreds or even thousands of unknown parameters.
** MCMC methods are primarily used for calculating [[Numerical analysis|numerical approximation]]s of [[Multiple integral|multi-dimensional integrals]], for example in [[Bayesian statistics]], [[computational physics]], [[computational biology]] and [[computational linguistics]]. <ref> See Gill 2008. </ref> <ref> See Robert & Casella 2004. </ref> * In [[Bayesian statistics]], the recent development of MCMC methods has been a key step in making it possible to compute large [[Bayesian network#Hierarchical models|hierarchical models]] that require integrations over hundreds or even thousands of unknown parameters.
*** They are also used for generating samples that gradually populate the rare failure region in [[rare event sampling]].
*** They are also used for generating samples that gradually populate the rare failure region in [[rare event sampling]].
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Latest revision as of 07:30, 22 August 2024

A MCMC Numerical Approximation Task is an random sample-based numerical approximation task that ...



References

2015

  1. See Gill 2008.
  2. See Robert & Casella 2004.