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17
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 33 (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,
Sensitivity analysis in Bayesian networks: From single to multiple parameters
 In 20’th Conference on Uncertainty in Artificial Intelligence (UAI
, 2004
"... Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the wo ..."
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Cited by 18 (2 self)
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Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the work to multiple parameters which may be in the CPT of a single variable, or the CPTs of multiple variables. Not only do we identify the solution space of multiple parameter changes that would be needed to enforce a query constraint, but we also show how to find the optimal solution, that is, the one which disturbs the current probability distribution the least (with respect to a specific measure of disturbance). We characterize the computational complexity of our new techniques and discuss their applications to developing and debugging Bayesian networks, and to the problem of reasoning about the value (reliability) of new information. 1
A method for evaluating elicitation schemes for probabilistic models
 Ieee Transactions on Systems Man and Cybernetics Part BCybernetics
"... [11] P. P. Shenoy, “Valuation network representation and solution of ..."
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Cited by 6 (0 self)
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[11] P. P. Shenoy, “Valuation network representation and solution of
A CostEffective Agent for Clinical Trial Assignment
, 2002
"... The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate medical histories. To select participants, the researchers analyze medical records of the available patients, which has traditionally been a ..."
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Cited by 6 (5 self)
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The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate medical histories. To select participants, the researchers analyze medical records of the available patients, which has traditionally been a manual procedure. We describe an intelligent agent that helps to select patients for clinical trials. If the available data are insufficient for choosing patients, the agent suggests additional medical tests and finds an ordering of the tests that reduces their total cost.
The Bayesian Advisor Project: Modeling Academic Advising
, 2001
"... An academic advisor's job requires that the advisor knows university and departmental requirements; available courses to satisfy those requirements, and a method for predicting relative student success for various course options. Such predictions are necessarily uncertain, both because th ..."
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Cited by 5 (3 self)
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An academic advisor's job requires that the advisor knows university and departmental requirements; available courses to satisfy those requirements, and a method for predicting relative student success for various course options. Such predictions are necessarily uncertain, both because the advisor has limited information about the student and because student performance can be influenced by unforeseen factors. In this work we present a preliminary Bayes Net model of academic advising. In it, the advisor's predictions are based on student performance in courses so far, as described in a student transcript. We describe the underlying model and some initial experiments. 1
Probability Assessment with Maximum Entropy in Bayesian Networks
 Computing Science and Statistics, volume 33. Interface
, 2001
"... Bayesian networks are widely accepted as tools for probabilistic modeling. ..."
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Cited by 3 (2 self)
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Bayesian networks are widely accepted as tools for probabilistic modeling.
Selection of patients for clinical trials: An interactive webbased system
 ARTIFICIAL INTELLIGENCE IN MEDICINE
, 2004
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A Method for Evaluating Elicitation Schemes for Probabilities
"... We present an objective approach for evaluating probabilit,, clicitati~m methods in probabilistic models. Our method draw~, tm ideas from research on learning Bayesian networks: iI ~c assume that the expert’s knowledge is manifested es~,cntially a.,, a database of records that have been collected i ..."
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We present an objective approach for evaluating probabilit,, clicitati~m methods in probabilistic models. Our method draw~, tm ideas from research on learning Bayesian networks: iI ~c assume that the expert’s knowledge is manifested es~,cntially a.,, a database of records that have been collected in the course of the expert’s experience, and if this database of records is available It:, us, then the ~tructure and parameters of the expert’s beliefs could be reliabl) ’ constructed using techniques for Bayesian learning from data. This learned model could, m turn. be compared to elicited models to judge the effectiveness of the clicitation process. We describe a general pn~ccdurc by which it is possible to capture the data corre,,ptmding to the expert’s beliefs, and we present a simple experiment in which we utilize this technique to compare three methods t~r eliciting discrete prt~babilities: (I) direct numerteal assc,,smcnt. (2) the probability wheel, and (3) the scaled iwohahihty bar. We show that t’or our domain, the scaled prub:lhlht} bar i~, the retest eflcctive tool for probability clicitdtlOn
BMAW11 Preface Preface
"... Bayesian networks are now a powerful, wellestablished technology for reasoning under uncertainty, supported by a wide range of mature academic and commercial software tools. They are now being applied in many domains, including environmental and ecological modelling, bioinformatics, medical decisio ..."
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Bayesian networks are now a powerful, wellestablished technology for reasoning under uncertainty, supported by a wide range of mature academic and commercial software tools. They are now being applied in many domains, including environmental and ecological modelling, bioinformatics, medical decision support, many types of engineering, robotics, military, financial and economic modelling, education, forensics, emergency response, surveillance, and so on. This workshop, the eighth in the series of workshops focusing on realworld applications of Bayesian networks, provides a focused, informal forum for discussion and interchange between researchers, practioners and tool developers. This year we encouraged the submission of papers addressing the workshop theme Knowledge Engineering, which we use as a general term that includes expert elicitation, learning from data, taking existing models from the literature, and any hybrids of these. Authors have been encouraged to describe the knowledge engineering process used to build their application, along with the pitfalls encountered and lessons learned. Papers in the workshop also address the practical issues involved in developing realworld applications, such as knowledge engineering methodologies, elicitation techniques, evaluation, and integration methods, with some describing software tools developed to these support these activities. Some papers describe
A Webbased Tool for Expert Elicitation in Distributed Teams
"... We present in this paper a webbased tool developed to enable expert elicitation of the probabilities associated with a Bayesian Network. The motivation behind this tool is to enable assessment of probabilities from a distributed team of experts when facetoface elicitation is not an option, for in ..."
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We present in this paper a webbased tool developed to enable expert elicitation of the probabilities associated with a Bayesian Network. The motivation behind this tool is to enable assessment of probabilities from a distributed team of experts when facetoface elicitation is not an option, for instance because of time and budget constraints. In addition to the ability to customize surveys, the tool provides support for both quantitative and qualitative elicitation, and offers administrative features such as elicitation surveys management and probability aggregation. 1