Results 1  10
of
21
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, ..."
Abstract

Cited by 28 (3 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 27 (4 self)
 Add to MetaCart
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.
Aggregating disparate estimates of chance
, 2004
"... We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We ad ..."
Abstract

Cited by 19 (4 self)
 Add to MetaCart
We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.
A dynamic Bayesian network for diagnosing ventilatorassociated pneumonia in ICU patients
, 2007
"... Diagnosing ventilatorassociated pneumonia in mechanically ventilated patients in intensive care units is seen as a clinical challenge. The difficulty in diagnosing ventilatorassociated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and tr ..."
Abstract

Cited by 6 (4 self)
 Add to MetaCart
Diagnosing ventilatorassociated pneumonia in mechanically ventilated patients in intensive care units is seen as a clinical challenge. The difficulty in diagnosing ventilatorassociated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and treating patients with pneumonia, a decisiontheoretic network had been designed with the help of domain experts. A major limitation of this network is that it does not represent pneumonia as a dynamic process that evolves over time. In this paper, we construct a dynamic Bayesian network that explicitly captures the development of the disease over time. We discuss how probability elicitation from domain experts served to quantify the dynamics involved and how the nature of the patient data helps reduce the computational burden of inference. We evaluate the diagnostic performance of our dynamic model for a number of real patients and report promising results.
Eliminating Incoherence from Subjective Estimates of Chance
 In: Proceedings of the 8th International Conference on the Principles of Knowledge Representation and Reasoning (KR
, 2002
"... Human judgment is an essential source of Bayesian probabilities but is plagued by incoherence when complex or conditional events are involved. We consider a method for adjusting estimates of chance over Boolean events so as to render them probabilistically coherent. The method works by searching for ..."
Abstract

Cited by 6 (4 self)
 Add to MetaCart
Human judgment is an essential source of Bayesian probabilities but is plagued by incoherence when complex or conditional events are involved. We consider a method for adjusting estimates of chance over Boolean events so as to render them probabilistically coherent. The method works by searching for a sparse distribution that approximates a target set of judgments. (We show that sparse distributions suce for this purpose.) The feasibility of our method was tested by randomly generating sets of coherent and incoherent estimates of chance over 30 to 50 variables.
Decision Support For Practical Reasoning: a theoretical and computational perspective
"... CONTENTS 1. Introduction 2. Practical Reasoning 3. Argument Schemes and Defeasibility 4. Decision Calculi 5. Reasoning under Resource Constraints 6. Moral Considerations 7. Deliberation Dialogue 8. Interface Design 9. Evaluation 10. Summary Group 2 2 Practical Reasoning 1 Introduction When faced w ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
CONTENTS 1. Introduction 2. Practical Reasoning 3. Argument Schemes and Defeasibility 4. Decision Calculi 5. Reasoning under Resource Constraints 6. Moral Considerations 7. Deliberation Dialogue 8. Interface Design 9. Evaluation 10. Summary Group 2 2 Practical Reasoning 1 Introduction When faced with difficult decisions about what to do, decision makers benefit from good advice. Good advice comes most reliably from advisors with relevant expertise. As well, good advice has at least three other essential features. First, the advice should be presented in a form which can be readily understood by the decision maker. Second, there should be ready access to both the information and the thinking that underpins the advice. Third, if decision making involves details which are at all unusual, the decision maker needs to be able to discuss those details with their advisors. Computer based systems are being increasingly used to assist people in decision making. Su
Verifying monotonicity in Bayesian networks with domain experts
 Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition
, 2006
"... In many realistic problem domains, the main variable of interest behaves monotonically in the observable variables, in the sense that higher values for the variable of interest become more likely with higherordered observations. This type of knowledge appears to naturally emerge from experts during ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
In many realistic problem domains, the main variable of interest behaves monotonically in the observable variables, in the sense that higher values for the variable of interest become more likely with higherordered observations. This type of knowledge appears to naturally emerge from experts during knowledge elicitation, without explicit prompting from the knowledge engineer. The experts ’ concept of monotonicity, however, may not correspond to the mathematical concept of monotonicity in Bayesian networks. We present a method that provides both for verifying whether or not a network exhibits the properties of monotonicity suggested by the experts and for studying the violated properties with the experts. We illustrate the application of our method for a real Bayesian network in veterinary science. 1
Building knowledgebased systems by credal networks: a tutorial
 ADVANCES IN MATHEMATICS RESEARCH
, 2010
"... Knowledgebased systems are computer programs achieving expertlevel competence in solving problems for specific task areas. This chapter is a tutorial on the implementation of this kind of systems in the framework of credal networks. Credal networks are a generalization of Bayesian networks where c ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Knowledgebased systems are computer programs achieving expertlevel competence in solving problems for specific task areas. This chapter is a tutorial on the implementation of this kind of systems in the framework of credal networks. Credal networks are a generalization of Bayesian networks where credal sets, i.e., closed convex sets of probability measures, are used instead of precise probabilities. This allows for a more flexible model of the knowledge, which can represent ambiguity, contrast and contradiction in a natural and realistic way. The discussion guides the reader through the different steps involved in the specification of a system, from the evocation and elicitation of the knowledge to the interaction with the system by adequate inference algorithms. Our approach is characterized by a sharp distinction between the domain knowledge and the process linking this knowledge to the perceived evidence, which we call the observational process. This distinction leads to a very flexible representation of both domain knowledge and knowledge about the way the information is collected, together with a technique to aggregate information coming from different sources. The overall procedure is illustrated throughout the chapter by a simple knowledgebased system for the prediction of the result of a football match.
Knowledge Engineering Tools for Probability Elicitation
, 2002
"... Unwieldy probability entry interfaces are the norm for Bayesian Network knowledge engineers. This requires domain experts to provide unreasonably accurate probability estimates. To solve these problems, we have developed two applications: CPTable improves direct probability entry, and provides nod ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Unwieldy probability entry interfaces are the norm for Bayesian Network knowledge engineers. This requires domain experts to provide unreasonably accurate probability estimates. To solve these problems, we have developed two applications: CPTable improves direct probability entry, and provides node customisation and sliding scale binary elicitation; Verbal Elicitor allows entry of probability values in ordinary English. The domain expert selects a verbal cue such as "unlikely" or "almost certain." The probabilities are then set manually or optimised to minimise probabilistic incoherency.