DeepAI AI Chat
Log In Sign Up

Expectation Consistent Approximate Inference: Generalizations and Convergence

by   Alyson K. Fletcher, et al.
University of California Santa Cruz
NYU college
The Ohio State University

Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. The proposed method, called Generalized Expectation Consistency (GEC), can be applied to both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimation. Here we characterize its fixed points, convergence, and performance relative to the replica prediction of optimality.


page 1

page 2

page 3

page 4


Approximate Survey Propagation for Statistical Inference

Approximate message passing algorithm enjoyed considerable attention in ...

An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing

Generalized Vector Approximate Message Passing (GVAMP) is an efficient i...

Expectation Propagation on the Maximum of Correlated Normal Variables

Many inference problems involving questions of optimality ask for the ma...

Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference

We design iterative receiver schemes for a generic wireless communicatio...

Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation

This paper presents a scalable approximate Bayesian method for image res...

Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation

Expectation propagation is a general prescription for approximation of i...

Expectation Consistent Plug-and-Play for MRI

For image recovery problems, plug-and-play (PnP) methods have been devel...