Generalization of graph network inferences in higher-order probabilistic graphical models

07/12/2021
by   Yicheng Fei, et al.
0

Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we construct iterative message-passing algorithms using Graph Neural Networks defined on factor graphs to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method gains advantage over Belief Propagation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/21/2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

A useful computation when acting in a complex environment is to infer th...
research
01/19/2022

Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism

Graphical models in probability and statistics are a core concept in the...
research
09/25/2013

Investigation of commuting Hamiltonian in quantum Markov network

Graphical Models have various applications in science and engineering wh...
research
02/08/2022

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX

PGMax is an open-source Python package for easy specification of discret...
research
01/16/2018

Factor graph fragmentization of expectation propagation

Expectation propagation is a general approach to fast approximate infere...
research
05/27/2011

Variational Cumulant Expansions for Intractable Distributions

Intractable distributions present a common difficulty in inference withi...
research
03/08/2016

Discriminative models for robust image classification

A variety of real-world tasks involve the classification of images into ...

Please sign up or login with your details

Forgot password? Click here to reset