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24
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,
Talking Probabilities: Communicating Probabilistic Information With Words And Numbers
 International Journal of Approximate Reasoning
, 1999
"... The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide ..."
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Cited by 27 (4 self)
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The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. The use of verbal probability expressions as an additional method of eliciting probabilistic information may to some extent remove this obstacle. In this paper, we review studies that address the communication of probabilities in words and/or numbers. We then describe our own experiments concerning the development of a probability scale that contains words as well as numbers. This scale appears to be an aid for researchers and domain experts during the elicitation phase of building a belief network and might help users understand the output of the network.
Interpreting Symptoms of Cognitive Load in Speech Input
 UM99, USER MODELING: PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE
, 1999
"... Users of computing devices are increasingly likely to be subject to situationally determined distractions that produce exceptionally high cognitive load. The question arises of how a system can automatically interpret symptoms of such cognitive load in the user's behavior. This paper examines thi ..."
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Cited by 23 (5 self)
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Users of computing devices are increasingly likely to be subject to situationally determined distractions that produce exceptionally high cognitive load. The question arises of how a system can automatically interpret symptoms of such cognitive load in the user's behavior. This paper examines this question with respect to systems that process speech input. First, we synthesize results of previous experimental studies of the ways in which a speaker's cognitive load is reflected in features of speech. Then we present a conceptualization of these relationships in terms of Bayesian networks. For two examples of such symptomssentence fragments and articulation ratewe present results concerning the distribution of the symptoms in realistic assistance dialogs. Finally, using artificial data generated in accordance with the preceding analyses, we examine the ability of a Bayesian network to assess a user's cognitive load on the basis of limited observations involving these two symptoms.
Properties of Sensitivity Analysis of Bayesian Belief Networks
 Proceedings of the Joint Session of the 6th Prague Symposium of Asymptotic Statistics and the 13th Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, Union of Czech Mathematicians and Physicists
, 1999
"... The assessments obtained for the various conditional probabilities of a Bayesian belief network inevitably are inaccurate. The inaccuracies involved influence the reliability of the network's output. By subjecting the belief network to a sensitivity analysis with respect to its conditional probabili ..."
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Cited by 11 (3 self)
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The assessments obtained for the various conditional probabilities of a Bayesian belief network inevitably are inaccurate. The inaccuracies involved influence the reliability of the network's output. By subjecting the belief network to a sensitivity analysis with respect to its conditional probabilities, the reliability of the output can be investigated. Unfortunately, straightforward sensitivity analysis of a Bayesian belief network is highly timeconsuming. In this paper, we show that, by qualitative considerations, several analyses can be identified as being uninformative as the conditional probabilities under study cannot affect the network's output. In addition, we show that the analyses that are informative comply with simple mathematical functions; more specifically, we show that the network's output can be expressed as a quotient of two functions that are linear in a conditional probability under study. These properties allow for considerably reducing the computational burden of se...
Integrating Bayesian Networks into KnowledgeIntensive CBR
 Proceedings of AAAI Workshop on CBR Integration
, 1998
"... In this paper we propose an approach to knowledge intensive CBR, where explanations are generated from a domain model consisting partly of a semantic network and partly of a Bayesian network (BN). The BN enables learning within this domain model based on the observed data. The domain model is used t ..."
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Cited by 10 (5 self)
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In this paper we propose an approach to knowledge intensive CBR, where explanations are generated from a domain model consisting partly of a semantic network and partly of a Bayesian network (BN). The BN enables learning within this domain model based on the observed data. The domain model is used to focus the retrieval and reuse of past cases, as well as the indexing when learning a new case. Essentially, the BNpowered submodel works in parallel with the semantic network model to generate a statistically sound contribution to case indexing, retrieval and explanation. 1. Introduction and
Intelligent probing: A costeffective approach to fault diagnosis in computer networks
 IBM SYSTEMS JOURNAL
, 2002
"... We consider the use of probing technology for fault diagnosis in computer networks. Probes are test transactions that can be actively selected and sent through the network. The use of probing technology for costeffective diagnosis requires addressing two issues: a planning phase in which the probes ..."
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Cited by 9 (0 self)
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We consider the use of probing technology for fault diagnosis in computer networks. Probes are test transactions that can be actively selected and sent through the network. The use of probing technology for costeffective diagnosis requires addressing two issues: a planning phase in which the probes are selected, followed by a diagnosis phase in which problem determination is performed using the results of the probes. The planning phase requires selecting a small but effective subset of all the possible probes. The diagnosis phase requires making inferences about the state of the network from the probe results in an environment of noise and uncertainty. This work addresses the probing problem using methods from artificial intelligence we call the resulting approach intelligent probing. The probes are selected by reasoning about the interactions between the probe paths. Although finding the optimal probe set is prohibitively expensive for large networks, we implement algorithms which find nearoptimal probe sets in linear time. In the diagnosis phase we use a Bayesian network approach and use a localinference approximation scheme that avoids the intractability of exact inference for large networks. Our results show that the quality of this approximate inference “degrades gracefully” under increasing uncertainty and increases as the quality of the probe set increases. 1
Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities
, 2001
"... Empirical study of sensitivity analysis on a Bayesian network examines the effects of varying the network’s probability parameters on the posterior probabilities of the true hypothesis. One appealing approach to modeling the uncertainty of the probability parameters is to add normal noise to the log ..."
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Cited by 5 (1 self)
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Empirical study of sensitivity analysis on a Bayesian network examines the effects of varying the network’s probability parameters on the posterior probabilities of the true hypothesis. One appealing approach to modeling the uncertainty of the probability parameters is to add normal noise to the logodds of the nominal probabilities. However, the paper argues that differences in sensitivities found on true hypothesis may only be valid in the range of standard deviations where the logodds normal distribution is unimodal. The paper also shows that using average posterior probabilities as criterion to measure the sensitivity may not be the most indicative, especially when the distribution is very asymmetric as is the case at nominal values close to zero or one. It is proposed, instead, to use the partial ordering of the most probable causes of diagnosis, measured by a suitable lower confidence bound. The paper also presents the preliminary results of our sensitivity analysis experiments with three Bayesian networks built for diagnosis of airplane systems. Our results show that some networks are more sensitive to imprecision in probabilities than previously believed.
Efficient fault diagnosis using probing
 In AAAI Spring Symposium on Information Refinement and Revision for Decision Making
, 2002
"... In this paper, we address the problem of efficient diagnosis in realtime systems capable of online information gathering, such as sending ”probes ” (i.e., test transactions, such as ”traceroute ” or ”ping”) in order to identify network faults and evaluate performance of distributed computer system ..."
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Cited by 4 (0 self)
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In this paper, we address the problem of efficient diagnosis in realtime systems capable of online information gathering, such as sending ”probes ” (i.e., test transactions, such as ”traceroute ” or ”ping”) in order to identify network faults and evaluate performance of distributed computer systems. We use a Bayesian network to model probabilistic relations between the problems (faults, performance degradation) and symptoms (probe outcomes). Due to intractability of exact probabilistic inference in large systems, we investigated approximation techniques, such as a localinference scheme called minibuckets(Dechter & Rish 1997). Our empirical study demonstrates advantages of local approximations for large diagnostic problems: the approximation is very efficient and ”degrades gracefully ” with noise; also, the approximation error gets smaller on networks with higher confidence (probability) of the exact diagnosis. Since the accuracy of diagnosis depends on how much information the probes can provide about the system states, the second part of our work is focused on the probe selection task. Small probe sets are desirable in order to minimize the costs imposed by probing, such as additional network load and data management requirements. Our results show that, although finding the optimal collection of probes is expensive for large networks, efficient approximation algorithms can be used to find a nearlyoptimal set.
Integrating and ranking uncertain scientific data
, 2008
"... Abstract — Mediatorbased data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling ..."
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Cited by 3 (2 self)
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Abstract — Mediatorbased data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict lessknown or previously unknown functions (though it does not improve predicting the wellknown). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for realworld problems as theory indicates. I.