The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

Important inference problems in statistical physics, computer vision, error-correcting coding
theory, and artificial intelligence can all be reformulated as the computation of marginal …

Graphical models such as factor graphs allow a unified approach to a number of key topics
in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

We introduce a new class of upper bounds on the log partition function of a Markov random
field (MRF). This quantity plays an important role in various contexts, including …

In 1955, McGill published a multivariate generalisation of Shannon's mutual information.
Algorithms such as Independent Component Analysis use a different generalisation, the …

Since its early ARPANET inception during the Cold War, the Internet has grown by a
staggering nine orders of magnitude. Today, the Internet and the World Wide Web pervade …

We present a tree-based reparameterization (TRP) framework that provides a new
conceptual view of a large class of algorithms for computing approximate marginals in …

We develop statistical methods which allow effective visual detection, categorization, and
tracking of objects in complex scenes. Such computer vision systems must be robust to wide …

We derive sufficient conditions for the uniqueness of loopy belief propagation fixed points.
These conditions depend on both the structure of the graph and the strength of the potentials …