Expectation Propagation for approximate Bayesian inference

Understand

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.
• All three algorithms try to recover an approximate distribution which is close in KL divergence to the true distribution.
• 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.