We have been developing a general symbolic-statistical modeling language [6, 19, 20] based on the logic programming framework that semantically uni es (and extends) major symbolic-statistical frameworks such as hidden Markov models (HMMs) [18], probabilistic contextfree grammars (PCFGs) [23] and Bayesian networks [16]. The language, PRISM, is intended to model complex symbolic phenomena governe...

We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm. Our algorithm represents the potential functions of the graphical model and all messages on the junction tree compactly as mixtures of rank-1 tensors. Using this representation, we show how to perform the operations required...

In the field of optimization using probabilistic models of the search space, this thesis identifies and elaborates several advancements in which the principles of maximum entropy and minimum relative entropy from information theory are used to estimate a probability distribution. The probability distribution within the search space is represented by a graphical model (factorization, Bayesian ne...

We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and...

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally...

Intelligent controller integrates in its structure in addition to standard attempt also principles of artificial intelligence, e.g. adaptive control, fuzzy logic, neural networks, quantitative control, etc. Implementation of such intelligent controllers to real process brings together some difficulties. Our approach, which is described as follows, allows testing of intelligent controllers in us...

This paper outlines a graphical model query approach based on graph matching. It consists of a graphical query specification language and a matching algorithm based on graph matching that takes the query as input and returns all matches found in a model to be searched. The graphical query specification language can be used to draw model queries much like a model would be constructed. To achieve...

When it comes to learning graphical models from data, approaches based on conditional independence tests are among the most popular methods. Since Bayesian networks dominate research in this field, these methods usually refer to directed graphs, and thus have to determine not only the set of edges, but also their direction. At least for a certain kind of possibilistic graphical models, however,...

Graphical models are widely used in scientific and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore...

As computer architectures transition towards exponentially increasing parallelism we are forced to adopt parallelism at a fundamental level in the design of machine learning algorithms. In this paper we focus on parallel graphical model inference. We demonstrate that the natural, synchronous parallelization of belief propagation is highly inefficient. By bounding the achievable parallel perform...