Approximation of probability distributions by convex mixtures of Gaussian measures

DOI10.1090/S0002-9939-10-10340-2MaRDI QIDQ3574837

Athanassia G. Bacharoglou

Publication date: 2 July 2010

Published in: Proceedings of the American Mathematical Society (Search for Journal in Brave)

zbMATH Keywords

mixtureprobability density functionnormal distributionuniversal seriesalgebraic genericity

Mathematics Subject Classification ID

Approximations to statistical distributions (nonasymptotic) (62E17) Approximation by other special function classes (41A30)

Related Items (9)

Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data ⋮ Universal approximation on the hypersphere ⋮ Semiparametric imputation using conditional Gaussian mixture models under item nonresponse ⋮ Generating probability distributions on intervals and spheres: convex decomposition ⋮ Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces ⋮ Approximation by finite mixtures of continuous density functions that vanish at infinity ⋮ Universal Taylor series without Baire and the influence of J.-P. Kahane ⋮ Consistency of variational Bayes inference for estimation and model selection in mixtures ⋮ Incremental Mixture Importance Sampling With Shotgun Optimization

Cites Work

• Universal series induced by approximate identities and some relevant applications
• Abstract theory of universal series and an application to Dirichlet series
• Universal series in ∩ _{p >1} ℓ ^p
• Abstract theory of universal series and applications
• Finite-dimensional sensor orbits and optimal nonlinear filtering
• Nonlinear Bayesian estimation using Gaussian sum approximations

This page was built for publication: Approximation of probability distributions by convex mixtures of Gaussian measures