Constraint-free Graphical Model with Fast Learning Algorithm

TL;DR

This paper introduces a constraint-free graphical model that simplifies learning the structure and parameters of multivariate distributions, utilizing local computations and information geometry principles.

Contribution

It presents a novel, versatile model that removes traditional constraints in Markov network learning, enabling simpler and faster algorithms based on local computations.

Findings

Algorithms perform effectively in experiments

Model simplifies structure and parameter learning

Removes complex constraints from Markov networks

Abstract

In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given data set is not a simple problem, because Markov networks rigorously represent Markov properties, and this rigor imposes complex constraints on the design of the networks. Our proposed model removes these constraints, acquiring important aspects from the information geometry. The proposed parameter- and structure-learning algorithms are simple to execute as they are based solely on local computation at each node. Experiments demonstrate that our algorithms work appropriately.