Bayes Factors for Forensic Decision Analyses with R [book review]

My friend EJ Wagenmaker pointed me towards an entire book on the BF by Bozza (from Ca’Foscari, Venezia), Taroni and Biederman. It is providing a sort of blueprint for using Bayes factors in forensics for both investigative and evaluative purposes. With R code and free access. I am of course unable to judge of the relevance of the approach for forensic science (I was under the impression that Bayesian arguments were usually not well-received in the courtroom) but find that overall the approach is rather one of repositioning the standard Bayesian tools within a forensic framework.

“The [evaluative] purpose is to assign a value to the result of a comparison between an item of unknown source and an item from a known source.”

And thus I found nothing shocking or striking from this standard presentation of Bayes factors, including the call to loss functions, if a bit overly expansive in its exposition. The style is also classical, with a choice of grey background vignettes for R coding parts that we also picked in our R books! If anything, I would have expected more realistic discussions and illustrations of prior specification across the hypotheses (see e.g. page 34), while the authors are mostly centering on conjugate priors and the (de Finetti) trick of the equivalent prior sample size. Bayes factors are mostly assessed using a conservative version of Jeffreys’ “scale of evidence”. The computational section of the book introduces MCMC (briefly) and mentions importance sampling, harmonic mean (with a minimalist warning), and Chib’s formula (with no warning whatsoever).

“The [investigative] purpose is to provide information in investigative proceedings (…) The scientist (…) uses the findings to generate hypotheses and suggestions for explanations of observations, in order to give guidance to investigators or litigants.”

Chapter 2 is about standard models: inferring about a proportion, with some Monte Carlo illustration,  and the complication of background elements, normal mean, with an improper prior making an appearance [on p.69] with no mention being made of the general prohibition of such generalised priors when using Bayes factors or even of the Lindley-Jeffreys paradox. Again, the main difference with Bayesian textbooks stands with the chosen examples.

Chapter 3 focus on evidence evaluation [not in the computational sense] but, again, the coverage is about standard models: processing the Binomial, multinomial, Poisson models, again though conjugates. (With the side remark that Fig 3.2 is rather unhelpful: when moving the prior probability of the null from zero to one, its posterior probability also moves from zero to one!) We are back to the Normal mean case with the model variance being known then unknown. (An unintentionally funny remark (p.96) about the dependence between mean and variance being seen as too restrictive and replaced with… independence!). At last (for me!), the book is pointing [p.99] out that the BF is highly sensitive to the choice of the prior variance (Lindley-Jeffreys, where art thou?!), but with a return of the improper prior (on said variance, p.102) with no debate on the ensuing validity of the BF. Multivariate Normals are also presented, with Wishart priors on the precision matrix, and more details about Chib’s estimate of the evidence. This chapter also contains illustrations of the so-called score-based BF which is simply (?) a Bayes factor using a distribution on a distance summary (between an hypothetical population and the data) and an approximation of the distributions of these summaries, provided enough data is available… I also spotted a potentially interesting foray into BF variability (Section 3.4.2), although not reaching all the way to a notion of BF posterior distributions.

Chapter 4 stands for Bayes factors for investigation, where alternative(s) is(are) less specified, as testing eg Basmati rice vs non-Basmati rice. But there is no non-parametric alternative considered in the book. Otherwise, it looks to me rather similar to Chapter 3, i.e. being back to binomial, multinomial models, with more discussions onm prior specification, more normal, or non-normal model, where the prior distribution is puzzingly estimated by a kernel density estimator, a portmanteau alternative (p.157), more multivariate Normals with Wishart priors and an entry on classification & discrimination.

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE. As appropriate for a book about Chance!]

Nature tidbits

Before returning a few older issues of Nature to the coffee room of the maths department, a quick look brought out the few following items of interests, besides the great cover above:

• France showing the biggest decline in overal output among the top 10 countries in the Nature Index Annual Tables.
• A tribune again the EU’s Plan S, towards funding (private) publishers directly from public (research) money. Why continuing to support commercial journals one way or another?!
• A short debate on geo-engineering towards climate control, with the dire warning that “little is known about the consequences” [which could be further damaging the chances of human survival on this planet].
• Another call for the accountability of companies designing AI towards fairness and unbiasedness [provided all agree on the meaning of these terms]
• A study that argues that the obesity epidemics is more prevalent in rural than urban areas due to a higher recourse to junk food in the former.
• A data mining venture in India to mine [not read] 73 million computerised journal articles, which is not yet clearly legal as the publishers object to it. Although the EU (and the UK) have laws authorising mining for non-commercial goals. (And India has looser regulations wrt copyright.)

Nature snapshots

In this 6 June issue of Nature, which I read on my way to O’Bayes, an editorial on the scary move by the WHO to incorporate traditional Chinese medicine remedies in its classification as this includes drugs made from protected and endangered species and as such remedies have not been evidence tested. A news brief on India abandoning the requirement for PhD students to get a paper published prior to been awarded the degree, presumably much to the sorrow of predatory publishers. A delay to Plan S (a European project to make all funded research freely available) reported to 21 January 2021. A review of the latest and yet unpublished book by Neal Stephenson, Fall. Which I obviously ordered immediately! A paper in the British Journal of Anasthesia published along with an independent assessment of the same study (methods and results). Some letters protesting the “public’s phobia” induced by the series Chernobyl. Which recoups an email from one of my colleagues on the same complaining theme, since “only 20 deaths” can be attributed to the disaster with certainty! A revisit of the “cold fusion” with no evidence of the claimed phenomenon that led to a scientific outcry in 1989.

Bayesian Inference, at £1400 per chapter…

Another unsolicited email (“This is a friendly reminder that….”) from a UK operator called IntechOpen about publishing a chapter in an Open Access book about Bayesian Inference. And edited by Tang Niansheng. As detailed on the publisher page

“The aim of this book is to introduce the newly developed Bayesian methods, including Bayesian variable selection (e.g., fixed-dimensional data analysis and high/ultrahigh dimensional data analysis), Bayesian influence analysis (e.g., case deletion method and local influence analysis), Bayesian estimation and clustering methods (e.g., fixed dimensional data, high/ultrahigh dimensional data analysis, Bayesian network, and Bayesian clustering for big data), Bayesian hypothesis test including discrete and continuous random variables, variational Bayesian analysis and Bayesian clinical trials including design and dose-finding algorithm”

which is neither off the mark nor particularly innovative, as Bayesian (hand?)books go. With the slight impediment of a £1400 fee per chapter. Even at the current exchange rate, this is far from “free of charge” (which only applies to the book being “free to download, read and share”. (Given the on-going upheavals of UK politics, it may soon become affordable, though!)

your interesting published article “An introduction to the special issue “

In the flow of unsolicited emails interested in publishing my work, a contender for the top call is this one as of today from Computer Communication & Collaboration that cites my foreword to the special issue of Statistics & Computing published out of the talks at MCMski IV in Chamonix. In 2014. (According to the above site, the publisher of the journal, Better Advances Press, does not meet most of its criteria and identified as predatory by Beall’s List, as of January 3, 2017.)

Your interesting published article “An introduction to the special issue “Joint IMS-ISBA meeting – MCMSki 4″” drives me to call for new papers, on behalf of Computer Communication & Collaboration, which is an English quarterly journal in Canada.

This peer-reviewed journal focuses on smart internet and it welcomes papers on general theories of computer science, data communications, multimedia, social network, machine learning, data mining, intelligent collaboration and other relevant topics, both theoretical and empirical.

All papers should be written in professional English. The length of 2000-6000 words is suggested. We accept papers in MS-word or PDF format.

If your paper is qualified for publication after refereeing, it will be published within 2-4 months from the date of submission.

Thank you for your consideration.