independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees

* * To whom correspondence should be addressed. Keywords: Maximum entropy discrimination Markov networks, Bayesian max-margin

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bioinformatics.

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects

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This paper explores the topic of sensitivity analysis in Markov networks, by tackling questions similar to those arising in the context of Bayesian networks: the tuning of parameters to satisfy query constraints, and the bounding of query changes when perturbing network parameters. Even though

Abstract We present an algorithm for learning high-treewidth Markov networks where inference is still tractable. This is made possible by exploiting context-specific independence and determinism in the domain. The class of models our algorithm can learn has the same desirable properties as thin

recognition is based on Hidden Markov Models the close interaction between the two components was termed Semantic Hidden Markov Networks. 1 INTRODUCTION Because of the high degree of uncertainty in the recognition of spoken language it is very important to exploit any possible predictions and restrictions

models, are unsuited for this task. In this paper we develop a consistent scheme for the important task of revising a Markov network so that it satisfies given (conditional) marginal distributions for some of the variables. This task is of high practical relevance as we demonstrate with a complex

, probabilistic entity-relationship models, Bayesian logic programs). In this chapter, we present a probabilistic modeling framework that builds on undirected graphical models (also known as Markov random fields or Markov networks). Undirected models address two limitations of the previous approach. First