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Model selection and accounting for model uncertainty in graphical models using Occam's window
, 1993
"... We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection o ..."
Abstract

Cited by 270 (46 self)
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We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection of a single model; inference is then conditional on the selected model. The sampling properties of such a strategy are complex, and the failure to take account of model uncertainty leads to underestimation of uncertainty about quantities of interest. In principle, a panacea is provided by the standard Bayesian formalism which averages the posterior distributions of the quantity of interest under each of the models, weighted by their posterior model probabilities. Furthermore, this approach is optimal in the sense of maximising predictive ability. However, this has not been used in practice because computing the posterior model probabilities is hard and the number of models is very large (often greater than 1011). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty byaveraging overamuch smaller set of models. An efficient search algorithm is developed for nding these models. We consider two classes of graphical models that arise in expert systems: the recursive causal models and the decomposable
Statistical Methods for Eliciting Probability Distributions
 Journal of the American Statistical Association
, 2005
"... Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting the experience of statisticia ..."
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Cited by 39 (2 self)
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Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively, and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful, i.e. what criteria should be employed? Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true ” in some objectivistic sense, and cannot be judged that way. We see elicitation as simply part of the process of statistical modeling. Indeed in a hierarchical model it is ambiguous at which point the likelihood ends and the prior begins. Thus the same kinds of judgment that inform statistical modeling in general also inform elicitation of prior distributions.
Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition
 IEEE Trans. Speech Audio Processing
, 1995
"... ..."
The Elicitation of Probabilities A Review of the Statistical Literature
, 2005
"... “We live in an uncertain world, and probability risk assessment deals as directly with that fact as anything we do. Uncertainty arises partly because we are fallible. ..."
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Cited by 3 (0 self)
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“We live in an uncertain world, and probability risk assessment deals as directly with that fact as anything we do. Uncertainty arises partly because we are fallible.
Eliciting multivariate probability distributions: Copulas and correlations
 in Wiley Encyclopedia of Operations Research and Management Science
, 2010
"... In this chapter we consider how to elicit a multivariate distribution to represent an expert’s uncertainty about a vector variable θ = (θ1,..., θd). This is an extension of the univariate case discussed in the previous chapter, which we assume the reader has studied. We again have three individuals ..."
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Cited by 1 (0 self)
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In this chapter we consider how to elicit a multivariate distribution to represent an expert’s uncertainty about a vector variable θ = (θ1,..., θd). This is an extension of the univariate case discussed in the previous chapter, which we assume the reader has studied. We again have three individuals in the elicitation process: a decisionmaker
Elicitation
, 2004
"... Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting both the experience of statis ..."
Abstract
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Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatterexpert colleagues. This paper reviews the stateoftheart, reflecting both the experience of statisticians and the fruits of a long line of psychological research into how people represent uncertain information cognitively, and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful, i.e. what criteria should be employed? Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true ” in some objectivistic sense, and cannot be judged that way. We see elicitation as simply part of the process of statistical modeling. Indeed in a hierarchical model it is ambiguous at which point the likelihood ends and the prior begins. Thus the same kinds of judgment that inform statistical modeling in general also inform elicitation of prior distributions.
On Eliciting Logistic Normal Priors for Multinomial Models
"... Multinomial models arise when there is a set of complementary and mutually exclusive categories and each observation falls into one of these categories. Such models are used in many scientific and industrial applications. For example, they are frequently applied to the compositions of rocks in geolo ..."
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Multinomial models arise when there is a set of complementary and mutually exclusive categories and each observation falls into one of these categories. Such models are used in many scientific and industrial applications. For example, they are frequently applied to the compositions of rocks in geology, to patterns of consumer selection preferences in microeconomics, and to voting behavior in
Nuclear Decommissioning Authority
"... Nuclear Energy within the Sixth Framework Programme (20022006) ..."