Forensic identification with imperfect evidence

Abstract

We study the problem of forensic identification when the trace evidence from the scene of the crime is imperfect: for example, it might be measured with error, or be partially missing. A general framework for imperfect data is developed, and applied in particular to the following cases, singly and in combination: 'measurement error' in the recorded information at the scene of the crime; 'binning', i.e. discretisation of an originally continuous crime measurement; 'paternity testing', in which the DNA profiles of the child and of the mother provide partial information on the true father's DNA; two types of 'laboratory error', one in which the error is equally distributed among all possible results, and a second in which there is a bias in the error mechanism producing a false match; and 'partial data', as when there is information on the offender's DNA profile, but not on his racial group.

Graphical Models in Genetics

“…The three grey nodes in (b) represent the observed genotypes and the black node is the 'query' node. Mortera, 1998) and especially when the possibility of observing a mutation from one generation to the next is considered (Dawid et al, 2001). Consider the inheritance claim case in Figure 24.15(a) from Dawid et al (2002), where a man, 9, claims to be the son of the diseased individual 3 and hence entitlement to part of his estate.…”

Applications and limitations of Forensic Biomechanics: A Bayesian perspective

“…In more recent years Bayes' Law has been used in forensic settings primarily to quantify probabilities associated with DNA testing, however its use has expanded to virtually any disputed issue that requires the quantification of probability and its complement, uncertainty (uncertainty = [1 À probability]). 31,32 At its most simple, Bayes' Law is stated P(A|B) in which the probability of A is dependent upon condition B, vs. the probability of A absent any conditions. For example, the probability of drawing an ace of hearts from a deck of cards is 1:52; however the probability of drawing an ace of hearts given that only red cards can be selected is 1:26, the probability of drawing the ace of hearts when only hearts are drawn is 1:13, and the probability of the ace of hearts when only aces are drawn is 1:4.…”

Applied forensic epidemiology: The Bayesian evaluation of forensic evidence in vehicular homicide investigation

“…9,10 With regard to forensic applications, Dawid has written extensively on the use of Bayes' theorem and other statistical arguments. 11,12 Although Bayes' law is known in forensic science primarily for its application to DNA evidence, Dawid has greatly advanced the use of Bayes' as a means of helping fact finders understand the impact of evidence using probability in a variety of applications, given the various ways that evidence can interact. 13 Bayes' law is, at its most simple, P(A|B) in which the probability of A is dependent upon condition B (for example, the probability of drawing an ace of spades from a deck of cards given that only black cards can be selected is 1:26).…”