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Blocking Gibbs Sampling in Very Large Probabilistic Expert Systems
 Internat. J. Human–Computer Studies
, 1995
"... We introduce a methodology for performing approximate computations in very complex probabilistic systems (e.g. huge pedigrees). Our approach, called blocking Gibbs, combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The methodology is illustrate ..."
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Cited by 46 (0 self)
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We introduce a methodology for performing approximate computations in very complex probabilistic systems (e.g. huge pedigrees). Our approach, called blocking Gibbs, combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The methodology is illustrated on a realworld problem involving a heavily inbred pedigree containing 20;000 individuals. We present results showing that blockingGibbs sampling converges much faster than plain Gibbs sampling for very complex problems.
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 ..."
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Cited by 30 (1 self)
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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.
Local Conditioning in Bayesian Networks
 Artificial Intelligence
, 1996
"... Local conditioning (LC) is an exact algorithm for computing probability in Bayesian networks, developed as an extension of Kim and Pearl's algorithm for singlyconnected networks. A list of variables associated to each node guarantees that only the nodes inside a loop are conditioned on the variable ..."
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Cited by 27 (6 self)
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Local conditioning (LC) is an exact algorithm for computing probability in Bayesian networks, developed as an extension of Kim and Pearl's algorithm for singlyconnected networks. A list of variables associated to each node guarantees that only the nodes inside a loop are conditioned on the variable which breaks it. The main advantage of this algorithm is that it computes the probability directly on the original network instead of building a cluster tree, and this can save time when debugging a model and when the sparsity of evidence allows a pruning of the network. The algorithm is also advantageous when some families in the network interact through AND/OR gates. A parallel implementation of the algorithm with a processor for each node is possible even in the case of multiplyconnected networks. 1 Introduction A Bayesian network is an acyclic directed graph in which every node represents a random variable, together with a probability distribution such that P (x 1 ; : : : ; x n ) = ...
Blocking Gibbs Sampling for Linkage Analysis in Large Pedigrees with Many Loops
 AMERICAN JOURNAL OF HUMAN GENETICS
, 1996
"... We will apply the method of blocking Gibbs sampling to a problem of great importance and complexity  linkage analysis. Blocking Gibbs combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The method is able to handle problems with very high complexi ..."
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Cited by 24 (2 self)
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We will apply the method of blocking Gibbs sampling to a problem of great importance and complexity  linkage analysis. Blocking Gibbs combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The method is able to handle problems with very high complexity such as linkage analysis in large pedigrees with many loops; a task that no other known method is able to handle. New developments of the method are outlined, and it is applied to a highly complex linkage problem.
On Test Selection Strategies for Belief Networks
, 1993
"... Decision making under uncertainty typically requires an iterative process of information acquisition. At each stage, the decision maker chooses the next best test (or tests) to perform, and reevaluates the possible decisions. Valueofinformation analyses provide a formal strategy for selecting the ..."
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Cited by 15 (7 self)
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Decision making under uncertainty typically requires an iterative process of information acquisition. At each stage, the decision maker chooses the next best test (or tests) to perform, and reevaluates the possible decisions. Valueofinformation analyses provide a formal strategy for selecting the next test(s). However, the complete decisiontheoretic approach is impractical and researchers have sought approximations. In this paper, we present strategies for both myopic and limited nonmyopic (working with known test groups) test selection in the context of belief networks. We focus primarily on utilityfree test selection strategies. However, the methods have immediate application to the decisiontheoretic framework. 9.1 Introduction Graphical belief network researchers have developed powerful algorithms to propagate the effects of any piece of information to all the variables in the model in a manner analogous to forward chaining in rulebased expert systems (see, for example, Daw...
Likelihood Computations Using Value Abstraction
 In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence
, 2000
"... In this paper, we use evidencespecific value abstraction for speeding Bayesian networks inference. ..."
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Cited by 8 (3 self)
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In this paper, we use evidencespecific value abstraction for speeding Bayesian networks inference.
A Simple Method for Finding a Legal Configuration in Complex Bayesian Networks
, 1996
"... This paper deals with an important problem with large and complex Bayesian networks. Exact inference in these networks is simply infeasible due to the huge storage requirements of exact methods. Markov chain Monte Carlo methods, however, are able to deal with these large networks but to do this they ..."
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Cited by 1 (0 self)
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This paper deals with an important problem with large and complex Bayesian networks. Exact inference in these networks is simply infeasible due to the huge storage requirements of exact methods. Markov chain Monte Carlo methods, however, are able to deal with these large networks but to do this they require an initial legal configuration to set off the sampler. So far nondeterministic methods like forward sampling have often been used for this even though the forward sampler may take an eternity to come up with a legal configuration. In this paper a novel algorithm will be presented that allows finding a legal configuration in a general Bayesian network in polynomial time in almost all cases. The algorithm will not be proven deterministic but empirical results will document that this holds in most cases. Also, the algorithm will be justified by its simplicity and ease of implementation. Keywords: Bayesian network, junction tree, pedigree analysis, Markov chain Monte Carlo, Gibbs samp...
Blocking Gibbs Sampling in Very Large . . .
 INTERNATIONAL JOURNAL OF HUMAN COMPUTER STUDIES. SPECIAL ISSUE ON REALWORLD APPLICATIONS OF UNCERTAIN REASONING
, 1995
"... We introduce a methodology for performing approximate computations in very complex probabilistic systems (e.g. huge pedigrees). Our approach, called blocking Gibbs, combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The methodology is illustrate ..."
Abstract
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We introduce a methodology for performing approximate computations in very complex probabilistic systems (e.g. huge pedigrees). Our approach, called blocking Gibbs, combines exact local computations with Gibbs sampling in a way that complements the strengths of both. The methodology is illustrated on a realworld problem involving a heavily inbred pedigree containing 20;000 individuals. We present results showing that blockingGibbs sampling converges much faster than plain Gibbs sampling for very complex problems.