About: dbpedia:Metropolis–Hastings_algorithm   Goto Sponge  NotDistinct  Permalink

An Entity of Type : owl:Thing, within Data Space : platform.yourdatastories.eu:8890 associated with source document(s)

In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value).

AttributesValues
rdfs:comment
  • In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value).
foaf:depiction
  • External Image
thumbnail
is known for of
Faceted Search & Find service v1.13.91 as of Nov 14 2017


Alternative Linked Data Documents: ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3212 as of Mar 29 2016, on Linux (x86_64-unknown-linux-gnu), Single-Server Edition (68 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2026 OpenLink Software