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DC Field | Value | Language |
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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 |
Appears in Collections: | School of Biological and Physical Sciences |
Files in This Item:
File | Description | Size | Format | |
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NR_GeccoPublication2022.pdf | 667.27 kB | Adobe PDF | View/Open |
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