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18
Building probabilistic networks: where do the numbers come from?  a guide to the literature
 IEEE Transactions on Knowledge and Data Engineering
, 2000
"... Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, ..."
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Cited by 29 (3 self)
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Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision,
A Probabilistic and DecisionTheoretic Approach to the Management of Infectious Disease at the ICU
 Artificial Intelligence in Medicine
, 2000
"... The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available online by an electronic clinical information system. In dataintensive clinical environments, such as intensive care units ..."
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Cited by 20 (9 self)
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The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available online by an electronic clinical information system. In dataintensive clinical environments, such as intensive care units (ICUs), clinical patient data are already fully managed by such systems in a number of hospitals. However, providing facilities for storing and retrieving patient data to clinicians is not enough; clinical information systems should also offer facilities to assist clinicians in dealing with hard clinical problems. Extending an information system's capabilities by integrating it with a decisionsupport system may be a solution. In this paper, we describe the development of a probabilistic and decisiontheoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensivecare unit. Its underlying probabilisticnetwork model includes tempo...
Bayesian Network Modelling through Qualitative Patterns
 ARTIFICIAL INTELLIGENCE
, 2003
"... In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative an ..."
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Cited by 12 (5 self)
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In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesiannetwork development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to o#er developers a highlevel starting point when developing Bayesian networks.
Bayesian Network Modelling by Qualitative Patterns
 In ECAI
, 2002
"... In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesiannetwork formalism and the features of the problem to be modelled. A notion that has been suggested in the literature to facilitate Bayesiannetwork d ..."
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Cited by 8 (3 self)
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In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesiannetwork formalism and the features of the problem to be modelled. A notion that has been suggested in the literature to facilitate Bayesiannetwork development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. However, only very few types of causal independence are in use today, as only the most obvious ones are really understood. We believe that qualitative probabilistic networks (QPNs) may be useful in helping understand causal independence. Originally, QPNs have been put forward as qualitative analogues to Bayesian networks. In this paper, we deploy QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to offer developers a highlevel starting point when developing Bayesian networks.
Certaintyfactorlike Structures in Bayesian Belief Networks
 KnowledgeBased Systems
, 2001
"... The certaintyfactor model was one of the most popular models for the representation and manipulation of uncertain knowledge in the early rulebased expert systems of the 1980s. After the model was criticised by researchers in artificial intelligence and statistics as being adhoc in nature, research ..."
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Cited by 6 (1 self)
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The certaintyfactor model was one of the most popular models for the representation and manipulation of uncertain knowledge in the early rulebased expert systems of the 1980s. After the model was criticised by researchers in artificial intelligence and statistics as being adhoc in nature, researchers and developers have stopped looking at the model. Nowadays, it is often stated that the model is merely interesting from a historical point of view. Its place has been taken over by more expressive formalisms for the representation and manipulation of uncertain knowledge, in particular, by the formalism of Bayesian belief networks. In this paper, it is shown that this view underestimates the importance of the principles underlying the certaintyfactor model. In particular, it is shown that certaintyfactorlike structures occur frequently in practical Bayesian network models as causal independence assumptions. In fact, the noisyOR and noisyAND models, two probabilistic models frequently employed, appear to be reinventions of combination functions previously introduced as part of the certaintyfactor model. This insight may lead to a reappraisal of the certaintyfactor model. 2001 Elsevier Science B.V. All fights reserved.
Towards effective elicitation of NINAND tree causal models
, 2009
"... Abstract. To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. It generally has the complexity exponential on n. NoisyOR reduces the complexity to linear, but can only represent reinforcing causal interacti ..."
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Cited by 5 (3 self)
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Abstract. To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. It generally has the complexity exponential on n. NoisyOR reduces the complexity to linear, but can only represent reinforcing causal interactions. The nonimpeding noisyAND (NINAND) tree is the first causal model that explicitly expresses reinforcement, undermining, and their mixture. It has linear complexity, but requires elicitation of a tree topology for types of causal interactions. We study their topology space and develop two novel techniques for more effective elicitation. 1
Y.: Enumerating unlabeled and root labeled trees for causal model acquisition
, 2009
"... Abstract. To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs to be assessed for each node. It generally has the complexity exponential on n. The nonimpeding noisyAND (NINAND) tree is a recently developed causal model that redu ..."
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Cited by 5 (4 self)
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Abstract. To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs to be assessed for each node. It generally has the complexity exponential on n. The nonimpeding noisyAND (NINAND) tree is a recently developed causal model that reduces the complexity to linear, while modeling both reinforcing and undermining interactions among causes. Acquisition of an NINAND tree model involves elicitation of a linear number of probability parameters and a tree structure. Instead of asking the human expert to describe the structure from scratch, in this work, we develop a twostep menu selection technique that aids structure acquisition. 1
Acquisition and Computation Issues with NINAND Tree Models
"... Most techniques to improve efficiency of conditional probability table (CPT) acquisition for Bayesian network (BN) can only represent reinforcing causal interactions. The nonimpeding noisyAND (NINAND) tree is the first causal model that explicitly expresses reinforcement, undermining, and their mi ..."
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Cited by 2 (2 self)
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Most techniques to improve efficiency of conditional probability table (CPT) acquisition for Bayesian network (BN) can only represent reinforcing causal interactions. The nonimpeding noisyAND (NINAND) tree is the first causal model that explicitly expresses reinforcement, undermining, and their mixture, while its acquisition is of linear complexity. We address three issues on acquisition and computation with these models. In particular, we propose methods to improve computation of conditional probability from a model, to improve the efficiency of CPT computation from these models, and to address NINAND tree acquisition by elicitation of pairwise causal interactions. 1
Relieving the elicitation burden of Bayesian Belief Networks
"... In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be specified in the quantification phase. This method enables the derivation of a variable’s co ..."
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Cited by 1 (1 self)
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In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be specified in the quantification phase. This method enables the derivation of a variable’s conditional probability table (CPT) in the general case that the states of the variable are ordered and the states of each of its parent nodes can be ordered with respect to the influence they exercise. EBBN requires only a limited amount of probability assessments from experts to determine a variable’s full CPT and uses piecewise linear interpolation. The number of probabilities to be assessed in this method is linear in the number of conditioning variables. EBBN’s performance was compared with the results achieved by applying both the normal copula vine approach from Hanea & Kurowicka (2007), and by using a simple uniform distribution. 1
Modelling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks
"... The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so f ..."
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Cited by 1 (1 self)
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The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions. 1