Proactive Message Passing on Memory Factor Networks

TL;DR

This paper presents a novel graphical model called memory factor networks (MFNs) and a proactive message passing (PMP) algorithm for efficient inference with convergence guarantees, applicable to various data types and problems.

Contribution

Introduction of memory factor networks and a proactive message passing algorithm with proven convergence for inference tasks.

Findings

PMP outperforms belief propagation variants in efficiency

MFNs effectively model data structures across multiple data types

PMP guarantees convergence during inference

Abstract

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.