Efficient belief propagation in second-order Bayesian networks for singly-connected graphs

Generalized belief propagation for approximate inference in hybrid Bayesian networks

Independency Relationships in Singly Connected Networks

Graphical structures such as causal networks or Markov networks are very useful tools for representing irrelevance or independency relationships. Singly connected networks are important speciic cases where there is no more than one undirected path connecting each pair of variables. The aim of this paper is to investigate the kind of properties that a dependency model must verify in order to be ...

Linearizing Belief Propagation for Efficient Label Propagation

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily (”birds of a feather flock together”) or heterophily (”opposites attract”). One of the most widely...

Belief Update in CLG Bayesian Networks With Lazy Propagation

In recent years Bayesian networks (BNs) with a mixture of continuous and discrete variables have received an increasing level of attention. We present an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG BNs). The architecture is an extension of lazy propagation using operations of Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator potentials i...

Loopy Belief Propagation in Bayesian Networks : origin and possibilistic perspectives

In this paper we present a synthesis of the work performed on two inference algorithms: the Pearl’s belief propagation (BP) algorithm applied to Bayesian networks without loops (i.e. polytree) and the Loopy belief propagation (LBP) algorithm (inspired from the BP) which is applied to networks containing undirected cycles. It is known that the BP algorithm, applied to Bayesian networks with loop...