Huber, Headrick & Bayes…

Huber and Headrick Handwriting Identification
Like many document examiners I consider Huber and Headrick’s 1999 textbook, Handwriting Identification: Facts and Fundamentals, to be a seminal work.1

In my opinion, it is the best textbook written to date on the topic of handwriting identification. The authors provide a comprehensive overview as well as some less conventional perspectives on certain concepts and topics.  In general I tend to agree with their position on many things. A bit of disclosure is need here: I was trained in the RCMP laboratory system; the same system in which Huber and Headrick were senior examiners and very influential.  Hence, I tend to be somewhat biased towards their point-of-view.

But that does not mean I think their textbook is perfect. While it is well written and manages to present a plethora of topics in reasonable depth, some parts are incomplete or misleading; particularly when we take developments that have happened since it was written into account.

One area of particular interest to me relates to the evaluation of evidence; specifically evaluation done using a coherent logical (or likelihood-ratio) approach.  I have posted elsewhere on the topic so I’m not going to re-hash the background or details any more than necessary.

Rev Bayes

This post will look at the topic of ‘Bayesian concepts’ as discussed by Huber and Headrick in their textbook.  These concepts fall under the general topic of statistical inference found in Chapter 4 “The Premises for the Identification of Handwriting”.  The sub-section of interest is #21 where the authors attempt to answer the question, “What Part Does Statistical Inference Play in the Identification Process?”  Much of their answer in that sub-section relates to Bayesian philosophy, in general, and the application of the logical approach to evidence evaluation.  However, while they introduce some things reasonably well, the discussion is ultimately very flawed and very much in need of correction. Or, at least, clarification.
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Propositions — key to the evaluation process

Scales balancing Evidence
One of the key elements in the logical approach to evidence evaluation are the propositions used for the evaluation. They are, in a certain sense, the most important part of the whole process. At the same time, they are also one of the least understood.

Today’s post explores the concept of propositions. I will attempt to describe what they are, how they are used, why we don’t change them once set and why they matter so much, among other things… all from the perspective of forensic document examination (and other forensic disciplines).
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When is a ‘Bayesian’ not a ‘Bayesian’?

Several of the posts on this blog relate to the logical approach to evidence evaluation; aka, the coherent logical approach, or the likelihood-ratio (LR) approach. In my opinion, it is the best way to evaluate evidence for forensic purposes no matter what type of evidence is being discussed. I say “best” because it is simple, logically sound, and relatively straight-forward to apply in forensic work.  It helps to promote transparency  through the application of a thorough and complete evaluation process (all points I have explained in other posts).

The reality is, however, that this approach is still not well understood by forensic practitioners, nor by members of the legal profession.

I hope that in time, and with education, that will change. Several workshops I have presented have been aimed at helping examiners understand what it really means, how it works, the philosophical basis behind the approach as well as the need for and benefit of doing things that particular way. It really does work to the benefit of both the examiner and their ultimate client, the court.

One recurring issue at these workshops relates to the very basic and fundamental concept of what the term “Bayesian” means. For various reasons, but mainly just misunderstanding, many people in the forensic document examination community hold the term “Bayesian” in negative regard. When the word ‘Bayes’, or any of its many derivations, come up in the conversation eyes glaze over while heads sag ever so slightly. And those are the positive people in the crowd.

I find such reactions understandable, but unfortunate.  The fact is that an understanding of the term is beneficial for anyone interested in how it might be applied in a forensic evidence context, whether or not one chooses to do so.  Indeed, for myself the answer to the question posed above — when is a Bayesian not a Bayesian? — lies in knowing how the overall Bayesian philosophy and theorem (or rule) differs from the more constrained and limited logical approach to evidence evaluation. These two are not the same or even close to equivalent.
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