Expectation Propagation for approximate Bayesian inference

@inproceedings{Minka2001ExpectationPF,
  title={Expectation Propagation for approximate Bayesian inference},
  author={Tom Minka},
  booktitle={UAI},
  year={2001}
}
This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation 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… CONTINUE READING
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