LEADER 05629nam 2200757Ia 450 001 9911020096703321 005 20230828200748.0 010 $a9786610648870 010 $a9781280648878 010 $a1280648872 010 $a9780470033319 010 $a0470033312 010 $a9780470033302 010 $a0470033304 035 $a(CKB)1000000000357088 035 $a(EBL)274315 035 $a(SSID)ssj0000263901 035 $a(PQKBManifestationID)11256304 035 $a(PQKBTitleCode)TC0000263901 035 $a(PQKBWorkID)10283142 035 $a(PQKB)10932683 035 $a(MiAaPQ)EBC274315 035 $a(OCoLC)85785187 035 $a(Perlego)2787947 035 $a(EXLCZ)991000000000357088 100 $a20060919d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aUncertain judgements $eeliciting experts' probabilities /$fAnthony O'Hagan ... [et al.] 210 $aLondon ;$aHoboken, NJ $cWiley$dc2006 215 $a1 online resource (339 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 08$a9780470029992 311 08$a0470029994 320 $aIncludes bibliographical references (p. 267-306) and indexes. 327 $aUncertain Judgements; Contents; Preface; 1 Fundamentals of Probability and Judgement; 1.1 Introduction; 1.2 Probability and elicitation; 1.2.1 Probability; 1.2.2 Random variables and probability distributions; 1.2.3 Summaries of distributions; 1.2.4 Joint distributions; 1.2.5 Bayes' Theorem; 1.2.6 Elicitation; 1.3 Uncertainty and the interpretation of probability; 1.3.1 Aleatory and epistemic uncertainty; 1.3.2 Frequency and personal probabilities; 1.3.3 An extended example; 1.3.4 Implications for elicitation; 1.4 Elicitation and the psychology of judgement 327 $a1.4.1 Judgement - absolute or relative?1.4.2 Beyond perception; 1.4.3 Implications for elicitation; 1.5 Of what use are such judgements?; 1.5.1 Normative theories of probability; 1.5.2 Coherence; 1.5.3 Do elicited probabilities have the desired interpretation?; 1.6 Conclusions; 1.6.1 Elicitation practice; 1.6.2 Research questions; 2 The Elicitation Context; 2.1 How and who?; 2.1.1 Choice of format; 2.1.2 What is an expert?; 2.2 The elicitation process; 2.2.1 Roles within the elicitation process; 2.2.2 A model for the elicitation process; 2.3 Conventions in Chapters 3 to 9; 2.4 Conclusions 327 $a2.4.1 Elicitation practice2.4.2 Research question; 3 The Psychology of Judgement Under Uncertainty; 3.1 Introduction; 3.1.1 Why psychology?; 3.1.2 Chapter overview; 3.2 Understanding the task and the expert; 3.2.1 Cognitive capabilities: the proper view of human information processing?; 3.2.2 Constructive processes: the proper view of the process?; 3.3 Understanding research on human judgement; 3.3.1 Experts versus the rest: the proper focus of research?; 3.3.2 Early research on subjective probability: 'conservatism' in Bayesian probability revision 327 $a3.4 The heuristics and biases research programme3.4.1 Availability; 3.4.2 Representativeness; 3.4.3 Do frequency representations remove the biases attributed to availability and representativeness?; 3.4.4 Anchoring-and-adjusting; 3.4.5 Support theory; 3.4.6 The affect heuristic; 3.4.7 Critique of the heuristics and biases approach; 3.5 Experts and expertise; 3.5.1 The heuristics and biases approach; 3.5.2 The cognitive science approach; 3.5.3 'The middle way'; 3.6 Three meta-theories of judgement; 3.6.1 The cognitive continuum; 3.6.2 The inside versus the outside view 327 $a3.6.3 The naive intuitive statistician metaphor3.7 Conclusions; 3.7.1 Elicitation practice; 3.7.2 Research questions; 4 The Elicitation of Probabilities; 4.1 Introduction; 4.2 The calibration of subjective probabilities; 4.2.1 Research methods in calibration research; 4.2.2 Calibration research: general findings; 4.2.3 Calibration research in applied settings; 4.2.4 A case study in probability judgement: calibration research in medicine; 4.3 The calibration of subjective probabilities: theories and explanations; 4.3.1 Explanations of probability judgement in calibration tasks 327 $a4.3.2 Theories of the calibration of subjective probabilities 330 $aElicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide 410 0$aStatistics in practice. 606 $aProbabilities 606 $aStatistics 606 $aDistribution (Probability theory) 606 $aMathematical statistics 606 $aBayesian statistical decision theory 615 0$aProbabilities. 615 0$aStatistics. 615 0$aDistribution (Probability theory) 615 0$aMathematical statistics. 615 0$aBayesian statistical decision theory. 676 $a519.54 701 $aO'Hagan$b Anthony$028422 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020096703321 996 $aUncertain judgements$94418097 997 $aUNINA