For marginal inference on graphical models, belief propagation (BP) has been the algorithm of choice due to impressive empirical results on many models. These models often contain many variables and factors, however the domain of each variable (the set of values that the variable can take

Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used

This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points

new model that is not far from propositionalized, therefore canceling the benefits of lifted inference. We present an algorithm, Anytime Lifted Belief Propagation, that corresponds to this intuition by performing shattering during belief propagation inference, on an as-needed basis, starting

complement conventional CS methods based on linear programming or greedy algorithms. We perform approximate Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast encoding and decoding is provided using sparse encoding matrices, which

belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis

properties: (i) they are convex, and have a unique global minimum; and (ii) the global minimum gives an upper bound on the log partition function. The global minimum is defined by stationary conditions very similar to those defining xed points of belief propagation (BP) or tree-based reparameterization [see

This papers proposes a new approximation method for Dempster-Shafer belief functions. The method is based on a new concept of incomplete belief potentials. It allows to compute simultaneously lower and upper bounds for belief and plausibility. Furthermore, it can be used for a resource

are computed (approximately) at once, using a twophase message-passing process along the cluster tree. The resulting approximation scheme is governed by a controlling parameter, "z" that allows adjustable levels of accuracy and efficiency, in "anytime" style. Empirical evaluation

Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized