BCMA-ES II: Revisiting Bayesian CMA-ES
Copyright policy )BCMA-ES II: Revisiting Bayesian CMA-ES
This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower in the normal Wishart prior model because of the convexity of the inverse. We present a mixture model that generalizes both normal Wishart and normal inverse Wishart model. We finally present various numerical experiments to compare both methods as well as the generalized method.
10 pages, 15 figures
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, 68T01, 68T20, Machine Learning (stat.ML), Machine Learning (cs.LG)
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, 68T01, 68T20, Machine Learning (stat.ML), Machine Learning (cs.LG)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!