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26
A Bayesian method for the induction of probabilistic networks from data
 Machine Learning
, 1992
"... Abstract. This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of ..."
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Cited by 1081 (27 self)
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Abstract. This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
Probabilistic Horn abduction and Bayesian networks
 Artificial Intelligence
, 1993
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesia ..."
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Cited by 298 (37 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...
Control of Selective Perception Using Bayes Nets and Decision Theory
, 1993
"... A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the neces ..."
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Cited by 100 (1 self)
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A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decisionmaking are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA1 selective vision system uses Bayes nets for representation and benefitcost analysis for control of visual and nonvisual actions. It is the highlevel control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA1 demonstrates that Bayes nets and decision theoretic techniques provide a general, reusable framework for constructi...
A Survey of Algorithms for RealTime Bayesian Network Inference
 In In the joint AAAI02/KDD02/UAI02 workshop on RealTime Decision Support and Diagnosis Systems
, 2002
"... As Bayesian networks are applied to more complex and realistic realworld applications, the development of more efficient inference algorithms working under realtime constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network ..."
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Cited by 32 (2 self)
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As Bayesian networks are applied to more complex and realistic realworld applications, the development of more efficient inference algorithms working under realtime constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network inference algorithms. In particular, previous research on realtime inference is reviewed. It provides a framework for understanding these algorithms and the relationships between them. Some important issues in realtime Bayesian networks inference are also discussed.
Semantic Translation Based on Approximate ReClassification
, 2000
"... We present a knowledgebased approachtointelligent information integration based on a reclassi#cation of information entities in a new context. We identify the needs of approaches performing semantic translation, weintroduce a uniform description of contexts and discuss a naive classi#cation a ..."
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Cited by 30 (9 self)
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We present a knowledgebased approachtointelligent information integration based on a reclassi#cation of information entities in a new context. We identify the needs of approaches performing semantic translation, weintroduce a uniform description of contexts and discuss a naive classi#cation approach. We argue that this approach makes unrealistic assumptions about the absence of uncertainty. Toovercome this problem we discuss several approximate classi#cation approaches and their use for information integration. Therebywe address symbolic as well as numeric approaches for uncertainty handling. We sumarize with a description of an actual application area and a discussion of open research topics.
Averagecase analysis of a search algorithm for estimating prior and posterior probabilities in Bayesian networks with extreme probabilities
, 1993
"... This paper provides a searchbased algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure ..."
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Cited by 29 (4 self)
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This paper provides a searchbased algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency. The algorithm is most suited to the case where we have extreme (close to zero or one) probabilities, as is the case in many diagnostic situations where we are diagnosing systems that work most of the time, and for commonsense reasoning tasks where normality assumptions (allegedly) dominate. We give a characterisation of those cases where it works well, and discuss how well it can be expected to work on average. 1 Introduction This paper provides a general purpose searchbased technique for computing posterior probabilities in arbitrarily structured discrete 1 Bayesian networks. Implementations of Bayesia...
Probabilistic Network Construction Using the Minimum Description Length Principle
, 1994
"... Probabilistic networks can be constructed from a database of cases by selecting a network that has highest quality with respect to this database according to a given measure. A new measure is presented for this purpose based on a minimum description length (MDL) approach. This measure is compared wi ..."
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Cited by 28 (1 self)
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Probabilistic networks can be constructed from a database of cases by selecting a network that has highest quality with respect to this database according to a given measure. A new measure is presented for this purpose based on a minimum description length (MDL) approach. This measure is compared with a commonly used measure based on a Bayesian approach both from a theoretical and an experimental point of view. We show that the two measures have the same properties for infinite large databases. For smaller databases, however, the MDL measure assigns equal quality to networks that represent the same set of independencies while the Bayesian measure does not. Preliminary test results suggest that an algorithm for learning probabilistic networks using the minimum description length approach performs comparably to a learning algorithm using the Bayesian approach. However, the former is slightly faster.
The use of conflicts in searching Bayesian networks
, 1993
"... This paper discusses how conflicts (as used by the consistencybased diagnosis community) can be adapted to be used in a searchbased algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime " algorithm, that at any stage can estimate the pro ..."
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Cited by 23 (3 self)
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This paper discusses how conflicts (as used by the consistencybased diagnosis community) can be adapted to be used in a searchbased algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime " algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorithm is most suited to the case with extreme probabilities. This paper presents a solution to the inefficiencies found in naive algorithms, and shows how the tools of the consistencybased diagnosis community (namely conflicts) can be used effectively to improve the efficiency. Empirical results with networks having tens of thousands of nodes are presented.
Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks
, 1996
"... This paper presents a search algorithm for estimating posterior probabilities in discrete Bayesian networks. It shows how conflicts (as used in consistencybased diagnosis) can be adapted to speed up the search. This algorithm is especially suited to the case where there are skewed distributions, al ..."
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Cited by 23 (6 self)
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This paper presents a search algorithm for estimating posterior probabilities in discrete Bayesian networks. It shows how conflicts (as used in consistencybased diagnosis) can be adapted to speed up the search. This algorithm is especially suited to the case where there are skewed distributions, although nothing about the algorithm or the definitions depends on skewness of distributions. The general idea is to forward simulate the network, based on the `normal' values for each variable (the value with high probability given its parents). When a predicted value is at odds with the observations, we analyse which variables were responsible for the expectation failure  these form a conflict  and continue forward simulation considering different values for these variables. This results in a set of possible worlds from which posterior probabilities  together with error bounds  can be 1 derived. Empirical results with Bayesian networks having tens of thousands of nodes are presented.
Uncertain Reasoning and Forecasting
 International Journal of Forecasting
, 1995
"... We develop a probability forecasting model through a synthesis of Bayesian beliefnetwork models and classical timeseries analysis. By casting Bayesian timeseries analyses as temporal beliefnetwork problems, weintroduce dependency models that capture richer and more realistic models of dynamic ..."
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Cited by 19 (3 self)
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We develop a probability forecasting model through a synthesis of Bayesian beliefnetwork models and classical timeseries analysis. By casting Bayesian timeseries analyses as temporal beliefnetwork problems, weintroduce dependency models that capture richer and more realistic models of dynamic dependencies. With richer models and associated computational methods, we can movebeyond the rigid classical assumptions of linearityin the relationships among variables and of normality of their probability distributions.