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Bayes’s theorem and weighing evidence by juries
 In R. Swinburne (Ed.), Bayes’s Theorem (pp. 7190). Proceedings of the British Academy
, 2002
"... At first sight, there may appear to be little connection between Statistics and Law. On closer inspection it can be seen that the problems they tackle are in many ways identical — although they go about them in very different ways. In a broad sense, each subject can be regarded as concerned with the ..."
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Cited by 11 (1 self)
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At first sight, there may appear to be little connection between Statistics and Law. On closer inspection it can be seen that the problems they tackle are in many ways identical — although they go about them in very different ways. In a broad sense, each subject can be regarded as concerned with the Interpretation of Evidence. I owe my own introduction to the common ground between the two activities to my colleague William Twining, Professor of Jurisprudence at University College London, who has long been interested in probability in the law. In our discussions we quickly came to realise that, for both of us, the principal objective in teaching our students was the same: to train them to be able to interpret a mixed mass of evidence. That contact led to my contributing some lectures on uses and abuses of probability and statistics in the law to the University of London Intercollegiate LlM course on Evidence and Proof (and an Appendix on “Probability and Proof ” to Anderson and Twining (1991)), as well as drawing me into related research (Dawid 1987; Dawid 1994; Dawid and Mortera 1996; Dawid and Mortera 1998). To my initial surprise, I found here a rich and stimulating source of problems, simultaneously practical and philosophical, to challenge my logical and analytical problemsolving skills. For general background on some of the issues involved, see Eggleston
SENSITIVITY OF INFERENCES IN FORENSIC GENETICS TO ASSUMPTIONS ABOUT FOUNDING GENES
, 2009
"... Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated—for exam ..."
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Cited by 10 (3 self)
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Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated—for example, when allele frequencies are unknown, there is identity by descent or the population is heterogeneous—dependence is generated among founding genes, that makes exact calculation of conditional probabilities by propagation methods less straightforward. Here we illustrate different methodologies for assessing sensitivity to assumptions about founders in forensic genetics problems. These include constrained steepest descent, linear fractional programming and representing dependence by structure. We illustrate these methods on several forensic genetics examples involving criminal identification, simple and complex disputed paternity and DNA mixtures.
Objectoriented Bayesian network for DNA mixture analyses
 BAYESIAN ANALYSIS
, 2006
"... In this paper we show how to represent with objectoriented Bayesian networks the mathematical model described in Cowell et al. (2006a), for identification problems involving DNA mixture traces. We present detailed descriptions of each component class used to build up the networks, and we apply the ..."
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In this paper we show how to represent with objectoriented Bayesian networks the mathematical model described in Cowell et al. (2006a), for identification problems involving DNA mixture traces. We present detailed descriptions of each component class used to build up the networks, and we apply the networks to an example.
Using Bayesian Networks for Paternity Calculations: Adding an Evolutionary Perspective
"... Bayesian Networks are gaining popularity as a graphical tool to communicate complex probabilistic reasoning required in the evaluation of DNA evidence. This study extends the current use of Bayesian Networks by incorporating the potential effects of evolution paternity calculations. Features of HUGI ..."
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Bayesian Networks are gaining popularity as a graphical tool to communicate complex probabilistic reasoning required in the evaluation of DNA evidence. This study extends the current use of Bayesian Networks by incorporating the potential effects of evolution paternity calculations. Features of HUGIN (a software package used to create Bayesian Networks) are demonstrated that have not, as yet, been explored. These features greatly simplify the process of building Bayesian Networks, allowing researchers to use these networks to solve new, more complex problems. Due to the increasing use of DNA evidence in courtrooms, and in light of recent studies on the potential impacts of ignoring evolution, this study is a natural extension to the body of research that already exists on Bayesian Networks. We explore three paternity examples, a simple case with two alleles, a simple case with multiple alleles, and a missing father case. Networks are built for each example which incorporate the effects of evolutionary relatedness. We then compare these new networks to previous networks.
© Institute of Mathematical Statistics, 2003 Graphical Models for Genetic Analyses
"... Abstract. This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separat ..."
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Abstract. This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating. Key words and phrases: Bayesian network, forensic genetics, linkage analysis, local computation, peeling, probability propagation, QTL analysis. 1.
unknown title
, 2013
"... Calculating and understanding the value of any type of match evidence when there are potential testing errors ..."
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Calculating and understanding the value of any type of match evidence when there are potential testing errors
myjournal manuscript No. (will be inserted by the editor) Linguistic Probabilities: Theory and Application
"... Abstract Over the past two decades a number of different approaches to “fuzzy probabilities ” have been presented. The use of the same term masks fundamental differences. This paper surveys these different theories, contrasting and relating them to one another. Problems with these existing approache ..."
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Abstract Over the past two decades a number of different approaches to “fuzzy probabilities ” have been presented. The use of the same term masks fundamental differences. This paper surveys these different theories, contrasting and relating them to one another. Problems with these existing approaches are noted and a theory of “linguistic probabilities ” is developed, which seeks to retain the underlying insights of existing work whilst remedying its technical defects. It is shown how the axiomatic theory of linguistic probabilities can be used to develop linguistic Bayesian networks which have a wide range of practical applications. To illustrate this a detailed and realistic example in the domain of forensic statistics is presented. 1