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
