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62
Why is Diagnosis Using Belief Networks Insensitive to Imprecision in Probabilities?
, 1996
"... Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, the authors have recently completed an extensive study in which they applied random noise to the numerical probabilities i ..."
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Cited by 36 (0 self)
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Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, the authors have recently completed an extensive study in which they applied random noise to the numerical probabilities in a set of belief networks for medical diagnosis, subsets of the CPCS network, a subset of the QMR (Quick Medical Reference) focused on liver and bile diseases. The diagnostic performance in terms of the average probabilities assigned to the actual diseases showed small sensitivity even to large amounts of noise. In this paper, we summarize the findings of this study and discuss possible explanations of this low sensitivity. One reason is that the criterion for performance is average probability of the true hypotheses, rather than average error in probability, which is insensitive to symmetric noise distributions. But, we show that even asymmetric, logoddsnormal noise has modest effects. A ...
A new probabilistic plan recognition algorithm based on string rewriting
 In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS
, 2008
"... This document formalizes and discusses the implementation of a new, more efficient probabilistic plan recognition algorithm called Yet Another Probabilistic Plan Recognizer, (Yappr). Yappr is based on weighted model counting, building its models using string rewriting rather than tree adjunction or ..."
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Cited by 36 (3 self)
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This document formalizes and discusses the implementation of a new, more efficient probabilistic plan recognition algorithm called Yet Another Probabilistic Plan Recognizer, (Yappr). Yappr is based on weighted model counting, building its models using string rewriting rather than tree adjunction or other tree building methods used in previous work. Since model construction is often the most computationally expensive part of such algorithms, this results in significant reductions in the algorithmâ€™s runtime.
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 32 (5 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.
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 31 (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,
When do Numbers Really Matter?
 Journal of Artificial Intelligence Research
, 2002
"... Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in c ..."
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Cited by 30 (7 self)
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Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.
A Review of Uncertainty Handling Formalisms
, 1998
"... Many different formal techniques, both numerical and symbolic, have been developed over the past two decades for dealing with incomplete and uncertain information. In this paper we review some of the most important of these formalisms, describing how they work, and in what ways they differ from one ..."
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Cited by 22 (1 self)
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Many different formal techniques, both numerical and symbolic, have been developed over the past two decades for dealing with incomplete and uncertain information. In this paper we review some of the most important of these formalisms, describing how they work, and in what ways they differ from one another. We also consider heterogeneous approaches which incorporate two or more approximate reasoning mechanisms within a single reasoning system. These have been proposed to address limitations in the use of individual formalisms.
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 prob ..."
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Cited by 13 (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...
Using Sensitivity Analysis for Efficient Quantification of a Belief Network
, 1999
"... Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience ..."
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Cited by 9 (0 self)
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Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorlyinformed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the wellinformed network quantification; these influential parameters were found by performing oneway sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highlyinformed network parameters to obtain a satisfying network quant...