Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Taroni, Franco; Biedermann, Alex; Bozza, Silvia; Garbolino, Paolo; Aitken, Colin

doi:10.1002/9781118914762

Cited by 75 publications

(86 citation statements)

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“…Available data is made of evidence ( E ) directly related to the crime, which helps discriminating h , and additional background data ( B ) not directly related to the crime and only pertaining to the nuisance parameter θ . This is partially different from the ‘background information’ I as defined in AITKEN and TARONI () and TARONI et al (), but often, background data can be thought of as part of the background information.…”

Section: The Full Bayesian Approach To Likelihood Ratiomentioning

confidence: 90%

“…(CEREDA, ) is concerned with the theoretical foundations of frequentist solutions, while this paper provides a careful derivation of the proper Bayesian LR for the rare type match problem described in section 3: the situation in which the Y‐STR profile of the crime stain and that of the suspect match, but they are not among the Y‐STR profiles observed in the reference database. In section 4, we will discuss the fact that influential Bayesian forensic literature (WEIR, ; AITKEN and TARONI, ; TARONI et al , ; SJERPS et al , ) seems to suggest the use of a frequentist‐defined LR (). They use Bayesian methodologies only inasmuch they provide a Bayesian estimate of θ to be plugged into .…”

Section: Introductionmentioning

confidence: 99%

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Bayesian approach to LR assessment in case of rare type match

Cereda

2017

Statistica Neerlandica

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The likelihood ratio (LR) is largely used to evaluate the relative weight of forensic data regarding two hypotheses, and for its assessment, Bayesian methods are widespread in the forensic field. However, the Bayesian ‘recipe’ for the LR presented in most of the literature consists of plugging‐in Bayesian estimates of the involved nuisance parameters into a frequentist‐defined LR: frequentist and Bayesian methods are thus mixed, giving rise to solutions obtained by hybrid reasoning. This paper provides the derivation of a proper Bayesian approach to assess LRs for the ‘rare type match problem’, the situation in which the expert wants to evaluate a match between the DNA profile of a suspect and that of a trace from the crime scene, and this profile has never been observed before in the database of reference. LR assessment using the two most popular Bayesian models (beta‐binomial and Dirichlet‐multinomial) is discussed and compared with corresponding plug‐in versions.

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Section: The Full Bayesian Approach To Likelihood Ratiomentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Bayesian approach to LR assessment in case of rare type match

Cereda

2017

Statistica Neerlandica

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“…Each of the issues has its own set of uncertainties and so there is something of a network of probabilities to be brought together. Much work has been done in the development of sophisiticated computer programs for construction of these 'Bayesian networks', and their implementation for the solution of forensic science problems is illustrated in many different situations by Taroni et al [17]. Such methods will contribute substantially to the construction and development of knowledge-based systems.…”

Section: (B) Bayesian Networkmentioning

confidence: 99%

The logical foundations of forensic science: towards reliable knowledge

Evett

2015

Phil. Trans. R. Soc. B

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The generation of observations is a technical process and the advances that have been made in forensic science techniques over the last 50 years have been staggering. But science is about reasoning —about making sense from observations. For the forensic scientist, this is the challenge of interpreting a pattern of observations within the context of a legal trial. Here too, there have been major advances over recent years and there is a broad consensus among serious thinkers, both scientific and legal, that the logical framework is furnished by Bayesian inference (Aitken et al. Fundamentals of Probability and Statistical Evidence in Criminal Proceedings ). This paper shows how the paradigm has matured, centred on the notion of the balanced scientist. Progress through the courts has not been always smooth and difficulties arising from recent judgments are discussed. Nevertheless, the future holds exciting prospects, in particular the opportunities for managing and calibrating the knowledge of the forensic scientists who assign the probabilities that are at the foundation of logical inference in the courtroom.

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“…By conducting multiple analyses attempting to span the space of assumptions meeting a specifed plausibility criterion, an analyst can purposefully explore the robustness of an interpretation. Presenting an uncertainty pyramid, along with an explanation of the corresponding plausibility criterion and a description of the data, may provide the audience the opportunity for greater understanding of the interactions among data, assumptions, and 8 More specifcally, the Dutch book arguments (Hájek [59]) 9 "I emphasize that the answers you give to the questions I ask you about your uncertainty are yours alone, and need not be the same as what someone else would say, even someone with the same information as you have, and facing the same decisions." -Kadane [23] 10 As a result of this common misunderstanding, we prefer phrases that use "plausible" in place of "validated."…”

Section: Discussionmentioning

confidence: 99%

Likelihood Ratio as Weight of Forensic Evidence: A Closer Look

Lund¹,

Iyer²

2017

J. RES. NATL. INST. STAN.

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The forensic science community has increasingly sought quantitative methods for conveying the weight of evidence. Experts from many forensic laboratories summarize their fndings in terms of a likelihood ratio. Several proponents of this approach have argued that Bayesian reasoning proves it to be normative. We fnd this likelihood ratio paradigm to be unsupported by arguments of Bayesian decision theory, which applies only to personal decision making and not to the transfer of information from an expert to a separate decision maker. We further argue that decision theory does not exempt the presentation of a likelihood ratio from uncertainty characterization, which is required to assess the ftness for purpose of any transferred quantity. We propose the concept of a lattice of assumptions leading to an uncertainty pyramid as a framework for assessing the uncertainty in an evaluation of a likelihood ratio. We demonstrate the use of these concepts with illustrative examples regarding the refractive index of glass and automated comparison scores for fngerprints.Key words: assumptions lattice; Bayes' factor; Bayes' rule; Bayesian decision theory; subjective probability; uncertainty; uncertainty pyramid. Executive SummaryIn response to calls from the broader scientifc community [1,2] and concerns of the general public, experts in many disciplines of forensic science have increasingly sought to develop and use objective or quantitative methods to convey the meaning of evidence to others, such as an attorney or members of a jury. Support is growing, especially in Europe [3,4], for a recommendation that forensic experts communicate their fndings using a "likelihood ratio" (see Appendix A for an introduction to likelihood ratios). Proponents of this approach [5 11] appear to believe that it is supported by Bayesian reasoning, a paradigm often viewed as normative (i.e., the right way; what someone should use) for making decisions when uncertainty exists [12 14].Individuals following Bayesian reasoning may establish their personal degrees of belief regarding the truth of a claim in the form of odds (i.e., ratio of their probability that the claim is true to their probability that the claim is false), taking into account all information currently available to them. Upon encountering new evidence, individuals quantify their "weight of evidence" as a personal likelihood ratio. Following Bayes' rule, individuals multiply their previous (or prior) odds by their respective likelihood ratios to obtain their updated (or posterior) odds, refecting their revised degrees of belief regarding the claim in question. Because the likelihood ratio is subjective and personal, we fnd that the proposed framework in which a forensic expert provides a likelihood ratio for others to use in Bayes' equation is unsupported by Bayesian Journal of Research of National Institute of Standards and Technology decision theory, which applies only to personal decision making and not to the transfer of information from an expert to a separate decision maker, s...

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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Cited by 75 publications

References 0 publications

Bayesian approach to LR assessment in case of rare type match

Bayesian approach to LR assessment in case of rare type match

The logical foundations of forensic science: towards reliable knowledge

Likelihood Ratio as Weight of Forensic Evidence: A Closer Look

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