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• W1934021597 abstract "This paper presents a new deterministic approximation technique in Bayesian networks. This method, Propagation, unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. Loopy belief propagation, because it propagates exact belief states, is useful for a limited class of belief networks, such as those which are purely discrete. Expectation Propagation approximates the belief states by only retaining expectations, such as mean and varitmce, and iterates until these expectations are consistent throughout the network. This makes it applicable to hybrid networks with discrete and continuous nodes. Experiments with Gaussian mixture models show Expectation Propagation to be donvincingly better than methods with similar computational cost: Laplace's method, variational Bayes, and Monte Carlo. Expectation Propagation also provides an efficient algorithm for training Bayes point machine classifiers." @default.
• W1934021597 created "2016-06-24" @default.
• W1934021597 creator A5044771560 @default.
• W1934021597 date "2001-08-02" @default.
• W1934021597 modified "2023-10-10" @default.
• W1934021597 title "Expectation propagation for approximate Bayesian inference" @default.
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