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25
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 32 (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 24 (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 14 (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.
Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation
, 2007
"... Representation of uncertain knowledge using a Bayesian network requires acquisition of a conditional probability table (CPT) for each variable. The CPT can be acquired by data mining or elicitation. When data are insufficient to support mining, causal modeling, such as the noisyOR, aids elicitati ..."
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Cited by 11 (6 self)
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Representation of uncertain knowledge using a Bayesian network requires acquisition of a conditional probability table (CPT) for each variable. The CPT can be acquired by data mining or elicitation. When data are insufficient to support mining, causal modeling, such as the noisyOR, aids elicitation by reducing the number of probability parameters to be acquired from human experts. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider causal interactions from the perspective of reinforcement or undermining. Our analysis shows that none can represent both interactions. Except the RNOR, other models also limit parameters to probabilities of singlecause events. We present the first general causal model, the nonimpeding noisyAND tree, that allows encoding of both reinforcement and undermining. It supports efficient CPT acquisition by elicitating a partial ordering of causes in terms of a tree topology, plus necessary numerical parameters. It also allows incorporation of probabilities for multicause events.
Functional specification of probabilistic process models
 In Proc. of AAAI05
"... Agents that handle complex processes evolving over a period of time need to be able to monitor the state of the process. Since the evolution of a process is often stochastic, this requires probabilistic monitoring of processes. A probabilistic process modeling language is needed that can adequately ..."
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Cited by 10 (3 self)
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Agents that handle complex processes evolving over a period of time need to be able to monitor the state of the process. Since the evolution of a process is often stochastic, this requires probabilistic monitoring of processes. A probabilistic process modeling language is needed that can adequately capture our uncertainty about the process execution. We present a language for describing probabilistic process models. This language is functional in nature, and the paper argues that a functional language provides a natural way to specify process models. In our framework, processes have both states and values. Processes may execute sequentially or in parallel, and we describe two alternative forms of parallelism. An inference algorithm is presented that constructs a dynamic Bayesian network, containing a variable for every subprocess that is executed during the course of executing a process. We present a detailed example demonstrating the naturalness of the language.
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.
Enumerating unlabeled and root labeled trees for causal model acquisition
, 2009
"... 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 c ..."
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Cited by 6 (5 self)
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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.
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 6 (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 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
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 5 (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.
Modeling causal reinforcement and undermining with noisyand trees
 Advances in Artificial Intelligence, LNAI 4013
, 2006
"... Abstract. Causal modeling, such as noisyOR, reduces probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions fr ..."
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Cited by 2 (1 self)
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Abstract. Causal modeling, such as noisyOR, reduces probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactions. We present the first explicit causal model that can encode both reinforcement and undermining and we show how to use such a model to support efficient probability elicitation. 1