Stochastic complexities of general mixture models in variational Bayesian learning

DOI10.1016/j.neunet.2006.05.030zbMath1112.68111OpenAlexW2030085689WikidataQ51935534 ScholiaQ51935534MaRDI QIDQ872400

Sumio Watanabe, Kazuho Watanabe

Publication date: 27 March 2007

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.05.030

zbMATH Keywords

free energyBayesian learningexponential familymixture modelvariational Bayesstochastic complexityKullback informationnon-regular model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Divergence measures and a general framework for local variational approximation ⋮ An alternative view of variational Bayes and asymptotic approximations of free energy ⋮ Comparing two Bayes methods based on the free energy functions in Bernoulli mixtures ⋮ Stochastic complexity for mixture of exponential families in generalized variational Bayes

Cites Work

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• Algebraic Analysis for Nonidentifiable Learning Machines
• Online Model Selection Based on the Variational Bayes
• Testing Homogeneity in Gamma Mixture Models
• Algorithmic Learning Theory
• On some inequalities for the gamma and psi functions

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