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Variational Bayes for mixture models: Reversing EM

By Thomas P. Minka


Bayesian calculations for mixture models are hampered by the fact that exact calculation of the necessary parameter integrals is exponentially complex. Variational lower bounds are a simple and efficient way to approximate such integrals (Attias, 1999). This note presents a general variational method, based on "reversing" EM, and its application to Gaussian and multinomial mixtures. Experiments show the benefits and drawbacks of lower bounds compared to Taylor expansion (Laplace's method)

Year: 2000
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