## Uncertain Judgements: Eliciting Experts' ProbabilitiesElicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide range of research findings in order to elicit expert judgements accurately and reliably. Uncertain Judgements introduces the area, before guiding the reader through the study of appropriate elicitation methods, illustrated by a variety of multi-disciplinary examples.
- Presenting a methodological framework for the elicitation of expert knowledge incorporating findings from both statistical and psychological research.
- Detailing techniques for the elicitation of a wide range of standard distributions, appropriate to the most common types of quantities.
- Providing a comprehensive review of the available literature and pointing to the best practice methods and future research needs.
- Using examples from many disciplines, including statistics, psychology, engineering and health sciences.
- Including an extensive glossary of statistical and psychological terms.
An ideal source and guide for statisticians and psychologists with interests in expert judgement or practical applications of Bayesian analysis, |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

1 | |

2 The Elicitation Context | 25 |

3 The Psychology of Judgement Under Uncertainty | 33 |

4 The Elicitation of Probabilities | 61 |

5 Eliciting Distributions General | 97 |

6 Eliciting and Fitting a Parametric Distribution | 121 |

7 Eliciting Distributions Uncertainty and Imprecision | 153 |

8 Evaluating Elicitation | 161 |

10 Published Examples of the Formal Elicitation of Expert Opinion | 193 |

11 Guidance on Best Practice | 217 |

12 Areas for Research | 223 |

Glossary | 227 |

Bibliography | 267 |

307 | |

313 | |

9 Multiple Experts | 179 |

### Other editions - View all

### Common terms and phrases

accuracy aleatory analysis anchor ask the expert availability heuristic Bayesian Bayesian statistics beta distribution biases calibration Chapter cognitive coherent conditional probabilities conjugate prior conjunction fallacy consider context correlation covariance credible intervals cues density function described discussed effect elicitation exercise elicitation method elicitation process elicited distribution elicited probabilities epistemic uncertainty estimate example expert’s beliefs expert’s distribution expert’s opinion expertise facilitator feedback forecasts frequency probability Garthwaite given heuristic imprecision individual instance interaction judge Kadane Kahneman knowledge linear mean measures median multivariate normal distribution O’Hagan observed occur outcome overconfidence overfitting parameters patients percentile posterior predictions prior distribution probabilistic probability assessments probability density function probability distribution probability judgements problem proportion psychology quantifying opinion quantiles questions random variable range regression relative frequency response sample scale scoring rule Section specified statistical statistician studies subjective probability summaries task theory tion Tversky typically uncertain quantities variance Winkler