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41
Exploiting Causal Independence in Bayesian Network Inference
 Journal of Artificial Intelligence Research
, 1996
"... A new method is proposed for exploiting causal independencies in exact Bayesian network inference. ..."
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Cited by 160 (9 self)
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A new method is proposed for exploiting causal independencies in exact Bayesian network inference.
A simple approach to Bayesian network computations
, 1994
"... The general problem of computing posterior probabilities in Bayesian networks is NPhard (Cooper 1990). However efficient algorithms are often possible for particular applications by exploiting problem structures. It is well understood that the key to the materialization of such a possibility is to ..."
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Cited by 82 (8 self)
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The general problem of computing posterior probabilities in Bayesian networks is NPhard (Cooper 1990). However efficient algorithms are often possible for particular applications by exploiting problem structures. It is well understood that the key to the materialization of such a possibility is to make use of conditional independence and work with factorizations of joint probabilities rather than joint probabilities themselves. Different exact approaches can be characterized in terms of their choices of factorizations. We propose a new approach which adopts a straightforward way for factorizing joint probabilities. In comparison with the clique tree propagation approach, our approach is very simple. It allows the pruning of irrelevant variables, it accommodates changes to the knowledge base more easily. it is easier to implement. More importantly, it can be adapted to utilize both intercausal independence and conditional independence in one uniform framework. On the other hand, clique tree propagation is better in terms of facilitating precomputations.
Efficient Reasoning in Qualitative Probabilistic Networks
 In Proceedings of the 11th National Conference on Artificial Intelligence (AAAI93
, 1993
"... Qualitative Probabilistic Networks (QPNs) are an abstraction of Bayesian belief networks replacing numerical relations by qualitative influences and synergies [ Wellman, 1990b ] . To reason in a QPN is to find the effect of new evidence on each node in terms of the sign of the change in belief (incr ..."
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Cited by 53 (7 self)
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Qualitative Probabilistic Networks (QPNs) are an abstraction of Bayesian belief networks replacing numerical relations by qualitative influences and synergies [ Wellman, 1990b ] . To reason in a QPN is to find the effect of new evidence on each node in terms of the sign of the change in belief (increase or decrease). We introduce a polynomial time algorithm for reasoning in QPNs, based on local sign propagation. It extends our previous scheme from singly connected to general multiply connected networks. Unlike existing graphreduction algorithms, it preserves the network structure and determines the effect of evidence on all nodes in the network. This aids metalevel reasoning about the model and automatic generation of intuitive explanations of probabilistic reasoning. Introduction A formal representation should not use more specificity than needed to support the reasoning required of it. The appropriate degree of specificity or numerical precision will vary depending on what kind o...
Generating Bayesian Networks from Probability Logic Knowledge Bases
 In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence
, 1994
"... We present a method for dynamically generating Bayesian networks from knowledge bases consisting of firstorder probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discretevalued nodes. We impose constraints on the fo ..."
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Cited by 53 (8 self)
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We present a method for dynamically generating Bayesian networks from knowledge bases consisting of firstorder probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discretevalued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of dseparation for knowledge bases and prove that a knowledge base with independence conditions defined by dseparation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P (QjE). We prove the algorithm to be correct. 1 Introduction The flexibility of Bayesian networks for representing probabilistic dependencies and the relative efficiency of computational techniques for p...
Bayesball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams
 In Uncertainty in Artificial Intelligence
, 1998
"... One of the benefits of belief networks and influence diagrams is that so much knowledge is captured in the graphical structure. In particular, statements of conditional irrelevance (or independence) can be verified in time linear in the size of the graph. To resolve a particular inference query or d ..."
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Cited by 45 (3 self)
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One of the benefits of belief networks and influence diagrams is that so much knowledge is captured in the graphical structure. In particular, statements of conditional irrelevance (or independence) can be verified in time linear in the size of the graph. To resolve a particular inference query or decision problem, only some of the possible states and probability distributions must be specified, the“requisite information.” This paper presents a new, simple, and efficient “Bayesball ” algorithm which is wellsuited to both new students of belief networks and state of the art implementations. The Bayesball algorithm determines irrelevant sets and requisite information more efficiently than existing methods, and is linear in the size of the graph for belief networks and influence diagrams.
A Computational Theory of Decision Networks
 International Journal of Approximate Reasoning
, 1994
"... This paper is about how to represent and solve decision problems in Bayesian decision theory (e.g. [6]). A general representation named decision networks is proposed based on influence diagrams [10]. This new representation incorporates the idea, from Markov decision process (e.g. [5]), that a decis ..."
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Cited by 33 (2 self)
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This paper is about how to represent and solve decision problems in Bayesian decision theory (e.g. [6]). A general representation named decision networks is proposed based on influence diagrams [10]. This new representation incorporates the idea, from Markov decision process (e.g. [5]), that a decision may be conditionally independent of certain pieces of available information. It also allows multiple cooperative agents and facilitates the exploitation of separability in the utility function. Decision networks inherit the advantages of both influence diagrams and Markov decision processes, which makes them a better representation framework for decision analysis, planning under uncertainty, medical diagnosis and treatment.
Welldefined Decision Scenarios
 In Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI–99
, 1999
"... Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over its past. However, some of the past may be ir ..."
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Cited by 29 (7 self)
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Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over its past. However, some of the past may be irrelevant for the decision, and for computational reasons it is important not to deal with redundant variables in the strategies. We show that current methods (e.g. the Decision Bayesball algorithm [Shachter, 1998]) do not determine the relevant past, and we present a complete algorithm. Actually, this paper takes a more general outset: When formulating a decision scenario as an influence diagram, a linear temporal ordering of the decisions variables is required. This constraint ensures that the decision scenario is welldefined. However, the structure of a decision scenario often yields certain decisions conditionally independent, and it is therefore unnecessary to impose a li...
Bayesian Network Analysis of Signaling Networks: A Primer
, 2005
"... Highthroughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological si ..."
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Cited by 24 (0 self)
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Highthroughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.
Lazy Evaluation of Symmetric Bayesian Decision Problems
 In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
, 1999
"... Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation  ..."
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Cited by 22 (13 self)
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Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation  a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the Hugin and valuationbased systems architectures for solving symmetric Bayesian decision problems. 1 INTRODUCTION Bayesian decision theory provides a solid foundation for assessing and thinking about actions under uncertainty. A symmetric Bayesian decision problem is specified with a set of decision variables, a set of chance...