Monday, 7 October 2019

Bayesian networks research on treating injured soldiers gains DoD funding


The research which this new US DoD funding supports is the continuation of a long term collaboration between the RIM (Risk and Information Management) Group at Queen Mary (with William Marsh taking the lead) and the Trauma Sciences Centre led by surgeon Col Nigel Tai.

The underlying AI decision support is provided by causal Bayesian Networks. Two of the previous models can be accessed and run online at www.traumamodels.com

Institute of Applied Data Science seminar: "Why machine learning from big data fails"

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On 3 October Norman Fenton gave a seminar: "Why machine learning from big data fails – and what to do about it", at the Institute for Applied Data Science, Queen Mary University. Here are the powerpoint slides for his presentation.

Tuesday, 24 September 2019

Naked Statistical Evidence

Consider the hypothetical scenario:
All 100 prisoners in a prison participate in a riot, and 99 of them participate in attacking and killing a guard (the other returned to his cell briefly after the riot). With the guard dead, all 100 prisoners then escape. The next day one of the prisoners is captured and charged with participating in the murder of the guard. While admitting to participating in the riot the prisoner claims that he was the one who was not involved in attacking the guard. In the absence of any other evidence there is 99% probability the prisoner is guilty. Is this sufficient to convict?
Christian Dahlman
The latest episode of the evidence podcast "Excited Utterance" has an excellent interview with our colleague Christian Dahlman of Lund University about this kind of "naked statistical evidence", available on itunes, and also here:

https://www.excitedutterancepodcast.com/listen

Christian contrasts the above kind of naked statistical evidence with forensic evidence, such as a footprint found at a crime scene whose pattern 'matches' that of a shoe worn by the suspect. Whereas the causal link between the statistical evidence and guilt goes from the former to the latter, the  causal link between the forensic evidence and guilt goes from the latter to the former:

This difference is central to the recent paper about the 'opportunity prior' that we co-authored with Christian. The fact that the suspect was at the prison means that he had the 'opportunity' to participate in the killing and that the prior probability for guilt given the naked statistical evidence is 99%.

Christian talks about his latest paper, and at the end of the interview (24:50), he defends the Bayesian approach to legal evidence against attacks from some legal scholars (this is something we also did in our recent paper on countering the ‘probabilistic paradoxes in legal reasoning’ with Bayesian networks).

References:


Sunday, 8 September 2019

Book Review: Pat Wiltshire’s “Traces: The memoirs of a forensic scientist and criminal investigator”


Gripping, scientifically rigorous and moving memoir of the world’s leading forensic palynologist. 

The quote on the back cover of this book says: “Nature will invariably give up her secrets to those of us who know where to look”. Pat Wiltshire, a truly ‘one of a kind’ forensic ecologist, is probably the most qualified person in the world when it comes to knowing where to look.

This book is both a (popular) science book and a personal life story. The science is a thorough introduction to multiple aspects of ecology (and notably palynology – the study of pollen and spores from plants and fungi) as well as a detailed description of the processes of forensic investigation and analysis. The personal story fully reveals how Pat become the person she is, including her motivations, regrets, and loves. The science and the memoirs are interwoven throughout the book and what links much of the narrative are the accounts of Pat’s forensic investigations that provide fascinating insights into a number of different crimes (including murders and rapes) that Pat has helped shed light on. There are also eight pages of colour photographs of Pat at most stages of her life in the middle of the book.

See the full review (on ResearchGate):
Book Review: Pat Wiltshire’s “Traces: The memoirs of a forensic scientist and criminal investigator”

Full pfd also available here: https://www.eecs.qmul.ac.uk/~norman/papers/Traces_Review.pdf

Note: There are different UK and US versions (with different titles). The US version has different grammar and no photographs, but unlike the UK version, the audio version is narrated by Pat

UK: “Traces: The memoirs of a forensic scientist and criminal investigator” Bonnier Books UK, 2019

USA: "Nature of Life and Death", Putnam House  G P Putnam's Sons 2019

Tuesday, 20 August 2019

Book Review: David Spiegelhalter’s “The Art of Statistics: How to Learn from Data”



A superb, timely overview of the benefits and limitations of statistics in the era of big data and machine learning

David Spiegelhalter has gained a deserved reputation as a masterful communicator of statistics and risk through his media work and writings. I believe this timely book is the best introduction to the benefits and limitations of statistics that I have seen and is David’s most important work yet in public communication. Any of the minor concerns explained in the review that I have about the book (including the understated role of causal models and the role of the likelihood ratio in courts) are the inevitable result of having to be selective about which more detailed material has to be left out to satisfy both the page and audience constraints.

In summary, this book is a must have for a) anybody who wants to better understand statistics and risk; b) anybody involved in the communication of statistics and risk; and c) anybody undertaking a course in data science and machine learning.

Here is the link to the full review:

Book Review: David Spiegelhalter’s “The Art of Statistics: How to Learn from Data” Pelican Books, 2019

See also:
Postscript: One of the slight concerns I discuss in the review related to the example of the use of statistics in identifying unusually poor hospital treatment outcomes. This is an example that I thought was crying out for a causal model along the lines of this:



Thursday, 4 July 2019

Challenging claims that probability theory is incompatible with legal reasoning

 

The published version of our paper "Resolving the so-called 'probabilistic paradoxes in legal reasoning' with Bayesian networks" is available for free download courtesy of Elsevier until 16 Aug. This is the link: https://authors.elsevier.com/c/1ZIQf4q6IcgUdA

The previous blog posting about this article is here.

The full citation:
de Zoete, J., Fenton, N. E., Noguchi, T., & Lagnado, D. A. (2019). "Countering the ‘probabilistic paradoxes in legal reasoning’ with Bayesian networks". Science & Justice 59 (4), 367-379,   10.1016/j.scijus.2019.03.003

Friday, 14 June 2019

Review of clinical practice guidelines for gestational diabetes


Gestational diabetes is the most common metabolic disorder of pregnancy, and it is important that well-written clinical practice guidelines (CPGs) are used to optimise healthcare delivery and improve patient outcomes. This paper published today in BMJ Open  is a review of such hospital-based CPGs. Seven CPGs met the criteria for inclusion in the review. Only two of these were considered to be of acceptable quality (one was from the Canadian Diabetic Association and other from the Auckland DHB, New Zealand).

Full reference citation:
Daley, B., Hitman, G., Fenton, N.E., & McLachlan, S. (2019). "Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes". BMJ Open, 9(6), e027285. https://doi.org/10.1136/bmjopen-2018-027285.  Fullpaper (pdf)
The work was funded by EPSRC as part of the PAMBAYESIAN project