graphs, a.k.a. Bayesian networks, and undirected graphs, a.k.a. Markov networks) Factor graphs allow to represent the product structure of a function. Example: consider the factorising …

In this paper, we describe the generic (naive and structured) variational method as message-passing algorithms on factor graphs; the factor graph notation allows a simpler formulation of …

This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to a Bayesian Network. Like belief propagation, Variational …

Jun 24, 2021 · In this paper, we develop a unifying account of constraint manipulation for variational inference in models that can be represented by a (Forney-style) factor graph, for …

Jan 1, 2019 · In this paper we focus on message passing in factor graphs as a platform for automated design of Bayesian inference algorithms. Message passing exploits local model …

Construct a graph data-structure from P that has a tree structure, and run message-passing on it! Bayesian estimate is a bit involved (due to non-conjugacy). We’ll come to it in GPs. How to...

Abstract: Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for approximating Bayesian inference in factorized probabilistic models that consist …

In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian Networks. Like belief propagation, VMP proceeds …

Variational Message Passing VMP makes it easier and quicker to apply factorised variational inference. VMP carries out variational inference using local computations and message …

A factor graph represents the factorization of a function of several variables. We use Forney-style factor graphs (Forney, 2001). Example: f(x 1,x 2,x 3,x 4,x 5) = f A(x 1,x 2,x 3)·f B(x 3,x 4,x 5)·f …