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[null,"1.","Record Nr.",null,"UNINA9910140644203321"]
[null,null,"Autore",null,"Balding D. J."]
[null,null,"Titolo",null,"Weight-of-evidence for forensic DNA profiles / / David J. Balding and Christopher D. Steele"]
[null,null,"Pubbl/distr/stampa",null,"Chichester, England : , : Wiley, , 2015"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"1-118-81451-7"]
[{"$nulls":4},"1-118-81454-1"]
[null,null,"Edizione",null,"[Second edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (234 p.)"]
[null,null,"Collana",null,"Statistics in Practice"]
[null,null,"Disciplina",null,"614.1"]
[null,null,"Soggetti",null,"Forensic genetics - Statistical methods"]
[{"$nulls":4},"DNA fingerprinting - Statistical methods"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright; Contents; Preface to the second edition; Preface to the first edition; Chapter 1 Introduction; 1.1 Weight-of-evidence theory; 1.2 About the book; 1.3 DNA profiling technology; 1.4 What you need to know already; 1.5 Other resources; Chapter 2 Crime on an island; 2.1 Warm-up examples; 2.1.1 People v. Collins (California, 1968); 2.1.2 Disease testing: positive predictive value (PPV); 2.1.3 Coloured taxis; 2.2 Rare trait identification evidence; 2.2.1 The 'island' problem; 2.2.2 A first lesson from the island problem; 2.3 Making the island problem more realistic"]
[{"$nulls":4},"2.3.1 The effect of uncertainty about p2.3.2 Uncertainty about N; 2.3.3 The effect of possible typing errors; 2.3.4 The effect of searches; 2.3.5 The effect of other evidence; 2.3.6 The effects of relatives and population subdivision; 2.4 Weight-of-evidence exercises; Chapter 3 Assessing evidence using likelihoods; 3.1 Likelihoods and their ratios; 3.2 The weight-of-evidence formula; 3.2.1 The population P; 3.2.2 Grouping the RX; 3.2.3 Application to the island problem; 3.3 General application of the formula; 3.3.1 Several items of evidence; 3.3.2 The role of the expert witness"]
[{"$nulls":4},"3.4 Consequences for DNA evidence 3.4.1 Many possible culprits; 3.4.2 Incorporating the non-DNA evidence; 3.4.3 Relatives; 3.4.4 Laboratory and handling errors; 3.4.5 Database searches; 3.5 Derivation of the weight-of-evidence formula 3.5.1 Bayes' theorem; 3.5.2 Uncertainty about p and N; 3.5.3 Grouping the alternative possible culprits; 3.5.4 "]
[{"$nulls":4},"Typing errors; 3.6 Further weight-of-evidence exercises; Chapter 4 Profiling technologies; 4.1 STR typing; 4.1.1 Anomalies; 4.1.2 Contamination; 4.1.3 Low-template DNA (LTDNA) profiling; 4.2 mtDNA typing; 4.3 Y-chromosome markers"]
[{"$nulls":4},"4.4 X-chromosome markers 4.5 SNP profiles 4.6 Sequencing  4.7 Methylation  4.8 RNA  4.9 Fingerprints  Chapter 5 Some population genetics for DNA evidence; 5.1 A brief overview; 5.1.1 Drift; 5.1.2 Mutation; 5.1.3 Migration; 5.1.4 Selection; 5.2 FST or θ; 5.2.1 Population genotype probabilities; 5.3 A statistical model and sampling formula; 5.3.1 Diallelic loci; 5.3.2 Multi-allelic loci; 5.4 Hardy-Weinberg equilibrium; 5.4.1 Testing for deviations from HWE  5.4.2 Interpretation of test results; 5.5 Linkage equilibrium; 5.6 Coancestry  5.6.1 One allele; 5.6.2 Two alleles"]
[{"$nulls":4},"5.6.3 Three alleles 5.6.4 General proof via recursion; 5.7 Likelihood-based estimation of FST 5.8 Population genetics exercises; Chapter 6 Inferences of identity; 6.1 Choosing the hypotheses; 6.1.1 Post-data equivalence of hypotheses; 6.2 Calculating LRs; 6.2.1 The match probability; 6.2.2 Single locus; 6.2.3 Multiple loci: the 'product rule'; 6.2.4 Relatives of Q; 6.2.5 Confidence limits 6.2.6 Other profiled individuals; 6.3 Application to STR profiles; 6.3.1 Values for the pj; 6.3.2 The value of FST; 6.3.3 Choice of population; 6.3.4 Errors; 6.4 Application to haploid profiles"]
[{"$nulls":4},"6.4.1 mtDNA profiles"]
[null,null,null,"Sommario/riassunto","DNA evidence is widely used in the modern justice system. Statistical methodology plays a key role in ensuring that this evidence is collected, interpreted, analysed and presented correctly. This book is a guide to assessing DNA evidence and presenting that evidence in a courtroom setting.  It offers practical guidance to forensic scientists with little dependence on mathematical ability, and provides the scientist with the understanding they require to apply the methods in their work. Since the publication of the first edition of this book in 2005 there have been many incremental changes"]
[null,"2.","Record Nr.",null,"UNINA9910810527703321"]
[null,null,"Autore",null,"Oelbermann Kai-Friederike"]
[null,null,"Titolo",null,"Biproportionale Divisormethoden und der Algorithmus der Alternierenden Skalierung / / Kai-Friederike Oelbermann"]
[null,null,"Pubbl/distr/stampa",null,"Berlin : , : Logos Verlag Berlin, , [2013]"]
[{"$nulls":4},"©2013"]
[null,null,"ISBN",null,"3-8325-9143-5"]
[null,null,"Descrizione fisica",null,"1 online resource (93 pages)"]
[null,null,"Collana",null,"Augsburger Schriften zur Mathematik, Physik und Informatik, , 1611-4256 ; ; 23"]
[null,null,"Disciplina",null,"330"]
[null,null,"Soggetti",null,"Economics"]
[{"$nulls":4},"Input-output analysis"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","PublicationDate: 20130810"]
[null,null,null,"Sommario/riassunto","Long description: Biproportionale Divisormethoden verrechnen Wählerstimmen in Sitzzahlen, sodass sowohl in jedem Wahlkreis ein vorgegebenes Sitzkontingent erfüllt wird als auch jeder Partei so viele Sitze zugeteilt werden, wie sich aus den Stimmen im gesamten Wahlgebiet ergeben.  Eingesetzt wird diese Methode bei einigen Schweizer Kantons- und Gemeinderatswahlen.   Zur Bestimmung einer biproportionalen Sitzzuteilung wird in dieser Arbeit der Algorithmus der alternierenden Skalierung (AS-Algorithmus) -- die diskrete Variante des iterativen proportionalen Anpassungsverfahrens (IPF-Verfahren) -- formal eingeführt. Dieser bestimmt iterativ skalierte Stimmenmatrizen, die nach Rundung alternierend die Wahlkreiskontingente und die Parteisitzzahlen erfüllen.    Die Analyse des AS-Algorithmus zeigt, dass er in der Praxis einwandfrei funktioniert. Mathematisch interessant sind die Fälle in denen die Sitzzuteilung aufgrund von Gleichständen nicht eindeutig ist. Es zeigt sich, dass der AS-Algorithmus in diesen Fällen unter Umständen versagt."]