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...

We consider the problem of learning Bayesian network classifiers that maximize the margin over a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected g...

The paper explores the potential of weighted best-first search schemes as anytime optimization algorithms for solving graphical models tasks such as MPE (Most Probable Explanation) or MAP (Maximum a Posteriori) and WCSP (Weighted Constraint Satisfaction Problem). While such schemes were widely investigated for path-finding tasks, their application for graphical models was largely ignored, possi...

We describe algorithms for maximum likelihood estimation of Gaussian graphical models with conditional independence constraints. It is well-known that this problem can be formulated as an unconstrained convex optimization problem, and that it has a closed-form solution if the underlying graph is chordal. The focus of this paper is on numerical algorithms for large problems with non-chordal grap...

In this paper, we address the problem of learning the structure of Gaussian chain graph models in a high-dimensional space. Chain graph models are generalizations of undirected and directed graphical models that contain a mixed set of directed and undirected edges. While the problem of sparse structure learning has been studied extensively for Gaussian graphical models and more recently for con...

Graphical games introduce a compact representation, where agents’ outcomes depend only on their neighbors. A distributed search algorithm for pure Nash equilibria of graphical games is presented. The algorithm uses the analogy of graphical games with asymmetric distributed constraints optimization problems (ADCOPs). The proposed algorithm includes three components an admissible pruning heuristi...

TRAPPER is a graphical environment aimed to support the programming of parallel embedded systems. TRAPPER comprises components for the design, mapping, visualization and optimization of parallel applications. One goal of this article is to motivate the requirements for a monitoring system in such a graphical programming environment. Because the existing monitoring system DELTA-T already fulfils...

Conventional compilers provide limited external control over the optimizations they automatically apply to attain high performance. Consequently, these optimizations have become increasingly ineffective due to the difficulty of understanding the higher-level semantics of the user applications. This paper presents a framework that provides interactive fine-grained control of compiler optimizatio...

: A new approach towards a model-based optimization of IC engine control on dynamometers is presented in this paper. The proposed methodology comprises advanced measurement strategies for a fast dynamic measurement of engine characteristics on dynamometers (DYNMET), a model-based offline optimization of feedforward control maps (STATOPT), and the optimization of dynamic transitions of turbochar...

The max-product algorithm, which attempts to compute the most probable assignment (MAP) of a given probability distribution, has recently found applications in quadratic minimization and combinatorial optimization. Unfortunately, the max-product algorithm is not guaranteed to converge and, even if it does, is not guaranteed to produce the MAP assignment. In this work, we provide a simple deriva...