Please use this identifier to cite or link to this item: http://ir.mu.ac.ke:8080/jspui/handle/123456789/6054
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dc.contributor.authorMilgo, Edna-
dc.contributor.authorOdoyo, Oyamo Reuben-
dc.contributor.authorManderick, Bernard-
dc.date.accessioned2022-03-03T15:41:36Z-
dc.date.available2022-03-03T15:41:36Z-
dc.date.issued2014-
dc.identifier.urihttps://sites.uclouvain.be/benelearn2014/wp-content/uploads/2014/06/proceedings.pdf#page=97-
dc.identifier.urihttp://ir.mu.ac.ke:8080/jspui/handle/123456789/6054-
dc.description.abstractBayesian reasoning and inference play a prominent role in machine learning (Andrieu et al., 2003). It requires evaluation of integrals of the form EX (f ) =∫ Ω f (x)π(dx) that in most cases cannot been done a- nalytically and are often high dimensional, the func- tion f has multiple modes and probability distribu- tion π has a heavy tail.en_US
dc.language.isoenen_US
dc.subjectMarkov Chain Monte Carlo (MCMC)en_US
dc.subjectPopulation and evolutionary MCMCsen_US
dc.titleEvolutionary MCMC revisited: A comparative studyen_US
dc.typeArticleen_US
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