Natural Gradient Evolution Strategies for Adaptive Sampling

dc.contributor.author	Kiprop, Ambrose
dc.contributor.author	Ronoh, NIxon
dc.contributor.author	Milgo, Edna
dc.contributor.author	Nowe, Ann
dc.contributor.author	Manderick, Bernard
dc.date.accessioned	2023-05-12T06:52:24Z
dc.date.available	2023-05-12T06:52:24Z
dc.date.issued	2022-07
dc.identifier.uri	http://ir.mu.ac.ke:8080/jspui/handle/123456789/7549
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. CCS CONCEPTS • Computing methodologies → Machine learning; Markov decision processes	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 Sampling	en_US
dc.type	Article	en_US