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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
BCMA-ES: A Bayesian approach to CMA-ES
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we d…
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that the posterior infere…
Bayesian InferenceRevisiting clustering as matrix factorisation on the Stiefel manifold
This paper studies clustering for possibly high dimensional data (e.g. images, time series, gene expression data, and many other settings), and rephrase it as low rank matrix estimation in the PAC-Bayesian framework. Our…
ClusteringTime SeriesTime Series AnalysisRevisiting Bayesian Autoencoders with MCMC
Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, t…
Bayesian InferenceDeep LearningDimensionality ReductionUncertainty QuantificationA New Bayesian Bootstrap for Quantitative Trade and Spatial Models
Economists use quantitative trade and spatial models to make counterfactual predictions. Because such predictions often inform policy decisions, it is important to communicate the uncertainty surrounding them. Three key …
counterfactual