Belief Propagation (BP) is a simple probabilistic inference algorithm, consisting of passing
messages between nodes of a graph representing a probability distribution. Its analogy with …

Graphical models are widely used in inference problems. In practice, one may construct a
single large-scale model to explain a phenomenon of interest, which may be utilized in a …

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance
could be poor in graphs with loops, and the understanding of its solution is limited. This work …

Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning
and inference in graphical models. While it is known to be exact on trees, in most …

In this article, we present a visual introduction to Gaussian Belief Propagation (GBP), an
approximate probabilistic inference algorithm that operates by passing messages between …

Belief propagation (BP) was only supposed to work for tree-like networks but works
surprisingly well in many applications involving networks with loops, including turbo codes …

Approximate inference in large and densely connected graphical models is a challenging
but highly relevant problem. Belief propagation, as a method for performing approximate …

Gaussian belief propagation (GaBP) is a message-passing algorithm that can be used to
perform approximate inference on a pairwise Markov graph (MG) constructed from a …

Belief Propagation (BP) is a widely used approximation for exact probabilistic inference in
graphical models, such as Markov Random Fields (MRFs). In graphs with cycles, however …

Belief propagation (BP) is an effective approximate inference method but lacks theoretical
guarantees for loopy graphs. We discuss the optimization landscape and the message …