Proactive Message Passing on Memory Factor Networks

by   Patrick Eschenfeldt, et al.

We introduce a new type of graphical model that we call a "memory factor network" (MFN). We show how to use MFNs to model the structure inherent in many types of data sets. We also introduce an associated message-passing style algorithm called "proactive message passing"' (PMP) that performs inference on MFNs. PMP comes with convergence guarantees and is efficient in comparison to competing algorithms such as variants of belief propagation. We specialize MFNs and PMP to a number of distinct types of data (discrete, continuous, labelled) and inference problems (interpolation, hypothesis testing), provide examples, and discuss approaches for efficient implementation.


page 20

page 23

page 29

page 30

page 31

page 32

page 33

page 34


Lifted Message Passing for the Generalized Belief Propagation

We introduce the lifted Generalized Belief Propagation (GBP) message pas...

Reactive Message Passing for Scalable Bayesian Inference

We introduce Reactive Message Passing (RMP) as a framework for executing...

Message passing for quantified Boolean formulas

We introduce two types of message passing algorithms for quantified Bool...

Efficient and accurate group testing via Belief Propagation: an empirical study

The group testing problem asks for efficient pooling schemes and algorit...

Continuous Graph Flow for Flexible Density Estimation

In this paper, we propose Continuous Graph Flow, a generative continuous...

High-dimensional macroeconomic forecasting using message passing algorithms

This paper proposes two distinct contributions to econometric analysis o...

Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference

We propose Unity Smoothing (US) for handling inconsistencies between a B...