Results 1  10
of
37
Mean Field Theory for Sigmoid Belief Networks
 Journal of Artificial Intelligence Research
, 1996
"... We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. ..."
Abstract

Cited by 116 (12 self)
 Add to MetaCart
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics.
Interpreting Bayesian Logic Programs
 PROCEEDINGS OF THE WORKINPROGRESS TRACK AT THE 10TH INTERNATIONAL CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING
, 2001
"... Various proposals for combining first order logic with Bayesian nets exist. We introduce the formalism of Bayesian logic programs, which is basically a simplification and reformulation of Ngo and Haddawys probabilistic logic programs. However, Bayesian logic programs are sufficiently powerful to ..."
Abstract

Cited by 109 (7 self)
 Add to MetaCart
Various proposals for combining first order logic with Bayesian nets exist. We introduce the formalism of Bayesian logic programs, which is basically a simplification and reformulation of Ngo and Haddawys probabilistic logic programs. However, Bayesian logic programs are sufficiently powerful to represent essentially the same knowledge in a more elegant manner. The elegance is illustrated by the fact that they can represent both Bayesian nets and definite clause programs (as in "pure" Prolog) and that their kernel in Prolog is actually an adaptation of an usual Prolog metainterpreter.
Graphical Models for Genetic Analyses
 STATISTTICAL SCIENCE
, 2003
"... This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas o ..."
Abstract

Cited by 28 (0 self)
 Add to MetaCart
This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating.
Decomposing Bayesian Networks: Triangulation of Moral Graph with Genetic Algorithms
 Statistics and Computing
, 1997
"... In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm ..."
Abstract

Cited by 22 (4 self)
 Add to MetaCart
In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NPhard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analyzed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjaerulff, 1990) and are comparable to the ones obtained with simulated annealing (Kjaerulff, 1990; Kjaerulff, 1992). Keywords: Bayesian networks, genetic algorithms, optimal decomposition, graph triangulation, moral graph, NPhard problems, statistical analysis. 1 Introduction The Bayesian networks constitute a reasoning method based on p...
CycleCutset sampling for Bayesian networks
 In The Canadian AI Conference, (CAAI’03
, 2003
"... The paper presents a new sampling methodology for Bayesian networks called cutset sampling that samples only a subset of the variables and applies exact inference for the others. We show that this approach can be implemented eciently when the sampled variables constitute a cyclecutset for the B ..."
Abstract

Cited by 17 (7 self)
 Add to MetaCart
The paper presents a new sampling methodology for Bayesian networks called cutset sampling that samples only a subset of the variables and applies exact inference for the others. We show that this approach can be implemented eciently when the sampled variables constitute a cyclecutset for the Bayesian network and otherwise it is exponential in the inducedwidth of the network's graph, whose sampled variables are removed. Cutset sampling is an instance of the well known RaoBlakwellisation technique for variance reduction investigated in [5, 2, 16]. Moreover, the proposed scheme extends standard sampling methods to nonergodic networks with ergodic subspaces. Our empirical results con rm those expectations and show that cycle cutset sampling is superior to Gibbs sampling for a variety of benchmarks, yielding a simple, yet powerful sampling scheme.
Modeling relations and their mentions without labeled text
 In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part III
, 2010
"... Abstract. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the ..."
Abstract

Cited by 17 (1 self)
 Add to MetaCart
Abstract. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraintdriven semisupervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a stateoftheart approach for relation extraction under distant supervision, we achieve 31 % error reduction. 1
Cutset sampling for Bayesian networks
 Journal of Artificial Intelligence Research
"... The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structureexploiting application of the RaoBlackwellisation principle to sampling in Bayesian networks. It improves conve ..."
Abstract

Cited by 14 (6 self)
 Add to MetaCart
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structureexploiting application of the RaoBlackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memorybased inference algorithms. It can also be viewed as an anytime approximation of the exact cutsetconditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loopcutset of the Bayesian network and, more generally, when the induced width of the network’s graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks. 1.
Importance Sampling Algorithms for the Propagation of Probabilities in Belief Networks
 INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
, 1994
"... This paper investigates the use of a class of importance sampling algorithms for probabilistic graphs in graphical structures. A general model for constructing importance sampling algorithms is given and then some particular cases are considered. Logical sampling and likelihood weighting are particu ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
This paper investigates the use of a class of importance sampling algorithms for probabilistic graphs in graphical structures. A general model for constructing importance sampling algorithms is given and then some particular cases are considered. Logical sampling and likelihood weighting are particular cases of the model. Our proposal will be an algorithm which uses the functions with less entropy (more informative) to simulate the variables and the functions with more entropy to weight the simulations, in this way we expec...
Sample Propagation
 Advances in Neural Information Processing System
, 2003
"... RaoBlackwellization is an approximation technique for probabilistic inference that flexibly combines exact inference with sampling. It is useful in models where conditioning on some of the variables leaves a simpler inference problem that can be solved tractably. This paper presents Sample Pro ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
RaoBlackwellization is an approximation technique for probabilistic inference that flexibly combines exact inference with sampling. It is useful in models where conditioning on some of the variables leaves a simpler inference problem that can be solved tractably. This paper presents Sample Propagation, an efficient implementation of RaoBlackwellized approximate inference for a large class of models. Sample Propagation tightly integrates sampling with message passing in a junction tree, and is named for its simple, appealing structure: it walks the clusters of a junction tree, sampling some of the current cluster's variables and then passing a message to one of its neighbors. We discuss the application of Sample Propagation to conditional Gaussian inference problems such as switching linear dynamical systems.