MCMC Numerical Approximation Task
A MCMC Numerical Approximation Task is an random sample-based numerical approximation task that ...
- Context:
- It can be solved by an MCMC Analysis System (that applies an MCMC analysis algorithm).
- See: Rare Event Sampling, Numerical Analysis, Multiple Integral, Bayesian Statistics, Computational Physics, Computational Biology, Computational Linguistics, Hierarchical Bayesian Model.
References
2015
- (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 approximations of multi-dimensional integrals, for example in Bayesian statistics, computational physics, computational biology and computational linguistics. [1] [2] * In Bayesian statistics, the recent development of MCMC methods has been a key step in making it possible to compute large 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.
- MCMC methods are primarily used for calculating numerical approximations of multi-dimensional integrals, for example in Bayesian statistics, computational physics, computational biology and computational linguistics. [1] [2] * In Bayesian statistics, the recent development of MCMC methods has been a key step in making it possible to compute large hierarchical models that require integrations over hundreds or even thousands of unknown parameters.