Please use this identifier to cite or link to this item:
http://ir.mu.ac.ke:8080/jspui/handle/123456789/6064
Title: | Adaptiveness of CMA based samplers |
Authors: | Milgo, Edna Ronoh, Nixon Waiganjo, Peter Manderick, Bernard |
Keywords: | Markov Chain Monte Carlo Sampling algorithm |
Issue Date: | 2017 |
Publisher: | ACM Digital Library |
Abstract: | We turn the Covariance Matrix Adaptation Evolution Strategy into an adaptive Markov Chain Monte Carlo (or MCMC) sampling algorithm that adapts online to the target distribution, i.e. the distribution to be sampled from. We call the resulting algorithm CMA-Sampling. It exhibits a higher convergence rate, a better mixing, and consequently a more effective MCMC sampler. We look at a few variants and compare their adaptiveness to a number of other adaptive samplers, including Haario et. al's AM sampler, on a testsuite of 4 target distributions. |
URI: | https://doi.org/10.1145/3067695.3075611 http://ir.mu.ac.ke:8080/jspui/handle/123456789/6064 |
Appears in Collections: | School of Information Sciences |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.