Stochastic complexities of general mixture models in variational Bayesian learning

Author:

Watanabe Kazuho,Watanabe Sumio

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference22 articles.

1. Nonmonotonic generalization bias of Gaussian mixture models;Akaho;Neural Computation,2000

2. Likelihood and Bayes procedure;Akaike,1980

3. On some inequalities for the Gamma and Psi functions;Alzer;Mathematics of Computation,1997

4. Attias, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of uncertainty in artificial intelligence (pp. 21–30)

5. Beal, M. J. (2003). Variational algorithms for approximate Bayesian inference. Ph.D. thesis. University College London

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