LEADER 02210nam0 22004453i 450 001 VAN00263214 005 20240806101511.640 017 70$2N$a9783540387794 100 $a20230913d1984 |0itac50 ba 101 $afre 102 $aDE 105 $a|||| ||||| 200 1 $aCones autopolaires et algebres de Jordan$fBruno Iochum 210 $aBerlin$cSpringer$d1984 215 $a247 p.$d24 cm 461 1$1001VAN00102250$12001 $aLecture notes in mathematics$1210 $aBerlin [etc.]$cSpringer$v1049 606 $a06-XX$xOrder, lattices, ordered algebraic structures [MSC 2020]$3VANC019973$2MF 606 $a06F25$xOrdered rings, algebras, modules [MSC 2020]$3VANC021771$2MF 606 $a17-XX$xNonassociative rings and algebras [MSC 2020]$3VANC021290$2MF 606 $a17C50$xJordan structures associated with other structures [MSC 2020]$3VANC037700$2MF 606 $a17C65$xJordan structures on Banach spaces and algebras [MSC 2020]$3VANC037674$2MF 606 $a46-XX$xFunctional analysis [MSC 2020]$3VANC019764$2MF 606 $a46A40$xOrdered topological linear spaces, vector lattices [MSC 2020]$3VANC022263$2MF 606 $a46Hxx$xTopological algebras, normed rings and algebras, Banach algebras [MSC 2020]$3VANC024201$2MF 606 $a46Lxx$xSelfadjoint operator algebras ($C^*$-algebras, von Neumann ($W^*$-) algebras, etc.) [MSC 2020]$3VANC020692$2MF 610 $aC-Algebra$9KW:K 610 $aJordan algebra$9KW:K 610 $aSelf-dual cone$9KW:K 610 $aW-Algebra$9KW:K 620 $dBerlin$3VANL000066 700 1$aIochum$bBruno$3VANV182212$057616 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20241115$gRICA 856 4 $uhttps://doi.org/10.1007/BFb0071358$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00263214 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-book 6602 $e08eMF6602 20230926 996 $aCônes autopolaires et algèbres de Jordan$980943 997 $aUNICAMPANIA LEADER 05754nam 22007334a 450 001 9911020144903321 005 20200520144314.0 010 $a9786610269303 010 $a9781280269301 010 $a1280269308 010 $a9780470092590 010 $a0470092599 010 $a9780470092606 010 $a0470092602 035 $a(CKB)111087027142156 035 $a(EBL)164885 035 $a(OCoLC)54357012 035 $a(SSID)ssj0000109295 035 $a(PQKBManifestationID)11138436 035 $a(PQKBTitleCode)TC0000109295 035 $a(PQKBWorkID)10045696 035 $a(PQKB)11616196 035 $a(MiAaPQ)EBC164885 035 $a(Perlego)2751696 035 $a(EXLCZ)99111087027142156 100 $a20030821d2004 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian approaches to clinical trials and health care evaluation /$fDavid J. Spiegelhalter, Keith R. Abrams, Jonathan P. Myles 210 $aChichester ;$aHoboken, NJ $cWiley$dc2004 215 $a1 online resource (408 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 08$a9780471499756 311 08$a0471499757 320 $aIncludes bibliographical references (p. 357-380) and index. 327 $aBayesian Approaches to Clinical Trials and Health-Care Evaluation; Contents; Preface; List of examples; 1 Introduction; 1.1 What are Bayesian methods?; 1.2 What do we mean by 'health-care evaluation'?; 1.3 A Bayesian approach to evaluation; 1.4 The aim of this book and the intended audience; 1.5 Structure of the book; 2 Basic Concepts from Traditional Statistical Analysis; 2.1 Probability; 2.1.1 What is probability?; 2.1.2 Odds and log-odds; 2.1.3 Bayes theorem for simple events; 2.2 Random variables, parameters and likelihood; 2.2.1 Random variables and their distributions 327 $a2.2.2 Expectation, variance, covariance and correlation 2.2.3 Parametric distributions and conditional independence; 2.2.4 Likelihoods; 2.3 The normal distribution; 2.4 Normal likelihoods; 2.4.1 Normal approximations for binary data; 2.4.2 Normal likelihoods for survival data; 2.4.3 Normal likelihoods for count responses; 2.4.4 Normal likelihoods for continuous responses; 2.5 Classical inference; 2.6 A catalogue of useful distributions*; 2.6.1 Binomial and Bernoulli; 2.6.2 Poisson; 2.6.3 Beta; 2.6.4 Uniform; 2.6.5 Gamma; 2.6.6 Root-inverse-gamma; 2.6.7 Half-normal; 2.6.8 Log-normal 327 $a2.6.9 Student's 2.6.10 Bivariate normal; 2.7 Key points; Exercises; 3 An Overview of the Bayesian Approach; 3.1 Subjectivity and context; 3.2 Bayes theorem for two hypotheses; 3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors; 3.4 Exchangeability and parametric modelling*; 3.5 Bayes theorem for general quantities; 3.6 Bayesian analysis with binary data; 3.6.1 Binary data with a discrete prior distribution; 3.6.2 Conjugate analysis for binary data; 3.7 Bayesian analysis with normal distributions; 3.8 Point estimation, interval estimation and interval hypotheses 327 $a3.9 The prior distribution 3.10 How to use Bayes theorem to interpret trial results; 3.11 The 'credibility' of significant trial results*; 3.12 Sequential use of Bayes theorem*; 3.13 Predictions; 3.13.1 Predictions in the Bayesian framework; 3.13.2 Predictions for binary data*; 3.13.3 Predictions for normal data; 3.14 Decision-making; 3.15 Design; 3.16 Use of historical data; 3.17 Multiplicity, exchangeability and hierarchical models; 3.18 Dealing with nuisance parameters*; 3.18.1 Alternative methods for eliminating nuisance parameters*; 3.18.2 Profile likelihood in a hierarchical model* 327 $a3.19 Computational issues 3.19.1 Monte Carlo methods; 3.19.2 Markov chain Monte Carlo methods; 3.19.3 WinBUGS; 3.20 Schools of Bayesians; 3.21 A Bayesian checklist; 3.22 Further reading; 3.23 Key points; Exercises; 4 Comparison of Alternative Approaches to Inference; 4.1 A structure for alternative approaches; 4.2 Conventional statistical methods used in health-care evaluation; 4.3 The likelihood principle, sequential analysis and types of error; 4.3.1 The likelihood principle; 4.3.2 Sequential analysis; 4.3.3 Type I and Type II error; 4.4 P-values and Bayes factors* 327 $a4.4.1 Criticism of P-values 330 $aREAD ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council's biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author's comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notab 410 0$aStatistics in practice. 606 $aBayesian statistical decision theory 606 $aMedicine$xResearch$xStatistical methods 606 $aClinical trials$xStatistical methods 615 0$aBayesian statistical decision theory. 615 0$aMedicine$xResearch$xStatistical methods. 615 0$aClinical trials$xStatistical methods. 676 $a519.5/42/02461 700 $aSpiegelhalter$b D. J$065673 701 $aAbrams$b K. R$g(Keith R.)$01837968 701 $aMyles$b Jonathan P$01837969 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020144903321 996 $aBayesian approaches to clinical trials and health care evaluation$94416842 997 $aUNINA