| 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 |