Expectation propagation on the diluted Bayesian classifier

Expectation Propagation for Bayesian Inference

Expectation Propagation(EP) is one of approaches for approximate inference which first formulated the way we see today at [1] though the idea has roots in many previous works in various areas. It can be considered as a variant of message-passing where each of the individual messages are approximated while being transferred. To introduce EP, it is easier to first start with a couple of approxima...

Expectation Propagation for Continuous Time Bayesian Networks

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is ...

Distributed Bayesian inference using expectation propagation

Expectation Propagation for approximate Bayesian inference

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 limi...

Approximate Expectation Propagation for Bayesian Inference on Large-scale Problems

where k indexes experimental replicates, i indexes the probe positions, j indexes the binding positions, andN ( jPj aji jjsjbj; i) represents the probability density function of a Gaussian distribution with mean Pj aji jjsjbj and variance i. We assign prior distributions on the binding event bj and the binding strength sj: p(bjj j) = bj j (1 j)1 bj (3) p0(sj) = Gamma(sjjc0; d0) (4) where Gamma(...