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http://ir.mu.ac.ke:8080/jspui/handle/123456789/6941Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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 |
| Appears in Collections: | School of Biological and Physical Sciences | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| NR_GeccoPublication2022.pdf | 667.32 kB | Adobe PDF | View/Open |
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