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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Milgo, Edna | - |
dc.contributor.author | Odoyo, Oyamo Reuben | - |
dc.contributor.author | Manderick, Bernard | - |
dc.date.accessioned | 2022-03-03T15:41:36Z | - |
dc.date.available | 2022-03-03T15:41:36Z | - |
dc.date.issued | 2014 | - |
dc.identifier.uri | https://sites.uclouvain.be/benelearn2014/wp-content/uploads/2014/06/proceedings.pdf#page=97 | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/6054 | - |
dc.description.abstract | Bayesian 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.iso | en | en_US |
dc.subject | Markov Chain Monte Carlo (MCMC) | en_US |
dc.subject | Population and evolutionary MCMCs | en_US |
dc.title | Evolutionary MCMC revisited: A comparative study | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Information Sciences |
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