Results 11  20
of
100
Network Engineering for Complex Belief Networks
 In Proc. UAI
, 1996
"... Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of ` ..."
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Cited by 34 (4 self)
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Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of `stubs' within a belief network. We propose an object oriented representation for belief networks which captures the semantic as well as representational knowledge embedded in the variables, their values and their parameters. Methods for evaluating complex networks are described. Throughout the discussion, tools which support the engineering of large belief networks are identified. 1. Introduction As belief networks become more popular and well understood as a tool for modeling uncertainty and as the computational power of belief network inference engines increases, belief networks are being applied to problems of increasing size and complexity. In the early 1990's, Pathfinder, at 109 nodes...
Maximal Prime Subgraph Decomposition of Bayesian Networks
 IEEE Transactions on Systems, Man, and Cybernetics, B
, 1999
"... In this paper we present a method for decomposition of Bayesian networks into their maximal prime subgraphs. The correctness of the method is proven and results relating the maximal prime subgraph decomposition to the maximal complete subgraphs of the moral graph of the original Bayesian network ..."
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Cited by 28 (0 self)
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In this paper we present a method for decomposition of Bayesian networks into their maximal prime subgraphs. The correctness of the method is proven and results relating the maximal prime subgraph decomposition to the maximal complete subgraphs of the moral graph of the original Bayesian network are presented. The maximal prime subgraphs of a Bayesian network can be organized as a tree which can be used as the computational structure for lazy propagation. We have also identified a number of tasks performed on Bayesian networks that can benefit from maximal prime subgraph decomposition. These tasks include divide and conquer triangulation, hybrid propagation algorithms combining exact and approximative inference techniques, and incremental construction of junction trees. Finally, we present the results of a series empirical evaluations relating the accumulated number of variables in maximal prime subgraphs of equal size to the size of the maximal prime subgraphs. 1 1
Topdown Construction and Repetitive Structures Representation in Bayesian Networks
 IN PROCEEDINGS OF THE THIRTEENTH INTERNATIONAL FLORIDA ARTIFICIAL INTELLIGENCE RESEARCH SOCIETY CONFERENCE
, 2000
"... Bayesian networks for large and complex domains are dicult to construct and maintain. For example modifying a small network fragment in a repetitive structure might be very time consuming. Topdown modelling may simplify the construction of large Bayesian networks, but methods (partly) supporti ..."
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Cited by 24 (3 self)
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Bayesian networks for large and complex domains are dicult to construct and maintain. For example modifying a small network fragment in a repetitive structure might be very time consuming. Topdown modelling may simplify the construction of large Bayesian networks, but methods (partly) supporting topdown modelling have only recently been introduced and tools do not exist. In this paper, we try to take a topdown approach to constructing Bayesian networks by using existing object oriented methods. We change these where they fail to support topdown modeling. This provides a new framework that allows topdown methodologies for the construction of Bayesian networks, provides an ecient class hierarchy and a compact way of specifying and representing temporal Bayesian networks. Furthermore, a conceptual simpli cation is achieved. Introduction Constructing and maintaining Bayesian networks (BNs) can be a time consuming process. Using current methods and tools, a topdown a...
An Importance Sampling Algorithm Based on Evidence PrePropagation
 In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence
, 2003
"... Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem... ..."
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Cited by 23 (4 self)
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Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem...
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.
Efficient computation for the noisy max
 International Journal of Intelligent Systems
, 2002
"... Díez’s algorithm for the noisy MAX is very efficient for polytrees, but when the network has loops it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as efficien ..."
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Cited by 21 (3 self)
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Díez’s algorithm for the noisy MAX is very efficient for polytrees, but when the network has loops it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as efficient for polytrees, but can be combined with general propagation algorithms, such as clustering or variable elimination, which are more efficient for networks with loops. In this paper we propose a new factorization of the noisy MAX that amounts to Díez’s algorithm in the case of polytrees and at the same time is more efficient than previous factorizations when combined with either variable elimination or clustering. 1
Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability
 In Proceedings of the 15th Conference on Uncertainty in Artifi cial Intelligence (UAI99
, 1999
"... Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty and are then now widely used in a variety of realworld tasks. Despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to the use ..."
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Cited by 19 (5 self)
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Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty and are then now widely used in a variety of realworld tasks. Despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to the use of BN for the socalled dependability analysis, i.e.
A Bayesian Network Approach To Making Inferences In Causal Maps
, 1999
"... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..."
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Cited by 18 (2 self)
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Causal Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Bayesian Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.1. Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
An Empirical Study of wCutset Sampling for Bayesian Networks
 IN UAI
, 2003
"... The paper studies empirically the timespace tradeoff between sampling and inference in the cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. As the size of the sampling space decreases, requiring less samp ..."
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Cited by 17 (9 self)
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The paper studies empirically the timespace tradeoff between sampling and inference in the cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. As the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. Algorithm wcutset sampling selects a sampling set such that the inducedwidth of the network when the sampling set is observed is bounded by w, thus requiring inference whose complexity is exponentially bounded by w. In this paper, we investigate the performance of wcutset sampling as a function of w. Our experiments over a range of randomly generated and real benchmarks, demonstrate the power of the cutset sampling idea and in particular show that an optimal balance between inference and sampling benefits substantially from restricting the cutset size, even at the cost of more complex inference.
A Causal Mapping Approach to Constructing Bayesian Networks
, 2000
"... Freix, for their comments and discussions. Nadkarni and Shenoy ii TABLE OF CONTENTS ..."
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Cited by 16 (0 self)
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Freix, for their comments and discussions. Nadkarni and Shenoy ii TABLE OF CONTENTS