Current methods for learning graphical models with latent variables and a
fixed structure estimate optimal values for the model parameters. Whereas this
approach usually produces overfitting and suboptimal generalization
performance, carrying out the Bayesian program of computing the full posterior
distributions over the parameters remains a difficult problem. Moreover,
learning the structure of models with latent variables, for which the Bayesian
approach is crucial, is yet a harder problem. In this paper I present the
Variational Bayes framework, which provides a solution to these problems. This
approach approximates full posterior distributions over model parameters and
structures, as well as latent variables, in an analytical manner without
resorting to sampling methods. Unlike in the Laplace approximation, these
posteriors are generally non-Gaussian and no Hessian needs to be computed. The
resulting algorithm generalizes the standard Expectation Maximization
algorithm, and its convergence is guaranteed. I demonstrate that this algorithm
can be applied to a large class of models in several domains, including
unsupervised clustering and blind source separation.

本文提出了变分贝叶斯 (Variational Bayes) 框架，通过解决概率图模型中潜在变量及其结构计算的问题，避免了因参数而导致过拟合和子最优泛化表现的通常方法，同时证明了该算法能成功应用于无监督聚类、盲源分离等模型。