Please use this identifier to cite or link to this item:
http://ir.mu.ac.ke:8080/jspui/handle/123456789/7549
Title: | Natural Gradient Evolution Strategies for Adaptive Sampling |
Authors: | Kiprop, Ambrose Ronoh, NIxon Milgo, Edna Nowe, Ann Manderick, Bernard |
Keywords: | Adaptive MCMC, Evolution strategies Natural gradient |
Issue Date: | Jul-2022 |
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 |
URI: | http://ir.mu.ac.ke:8080/jspui/handle/123456789/7549 |
Appears in Collections: | School of Biological and Physical Sciences |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
NR_GeccoPublication2022.pdf | 667.27 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.