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ECML
2005
Springer

U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models

9 years 5 months ago
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimate of parameters from this distribution. Our approach differs from the Bayesian and Maximum Likelihood (ML) approaches. It gives an alternative to Bayesian inference when we don’t want to define a prior over parameters and gives an alternative to the ML method when we want a better estimate of the distribution over hidden variables. As a practical implementation, we present a Uupdating algorithm based on the mean field theory to approximate the distribution over hidden variables from the U-likelihood. This algorithm captures some of the correlations among hidden variables by estimating reaction terms. Those reaction terms are found to penalize the likelihood. We show that the U-updating algorithm becomes the EM algorithm as a special case in the large sample limit. The useful behavior of our method is con...
JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ECML
Authors JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin Ghahramani
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