Natural gradient evolution strategies for adaptive samplin

dc.contributor.author	Kiprop, Ambrose K.
dc.contributor.author	Ronoh, Nixon
dc.contributor.author	Milgo, Edna
dc.date.accessioned	2022-10-19T07:11:40Z
dc.date.available	2022-10-19T07:11:40Z
dc.date.issued	2022
dc.identifier.uri	http://ir.mu.ac.ke:8080/jspui/handle/123456789/6941
dc.description.abstract	We evaluate two (1+1)-natural evolution strategies (NES) turned into adaptive Markov chain Monte Carlo (MCMC) samplers on a test suite of probability distributions. We compare their performance with the AM-family of samplers considered to be the state of the art in adaptive MCMC. Our experiments show that natural gradient-based adaptation used in NES further improves adaptive MCMC	en_US
dc.language.iso	en	en_US
dc.subject	Adaptive MCMC	en_US
dc.subject	Evolution strategies	en_US
dc.subject	Natural gradient	en_US
dc.title	Natural gradient evolution strategies for adaptive samplin	en_US
dc.type	Article	en_US