Archive for statistical evidence

dodging bullets, IEDs, and fingerprint detection at SimStat19

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , on September 10, 2019 by xi'an

I attended a fairly interesting forensic science session at SimStat 2019 in Salzburg as it concentrated on evidence and measures of evidence rather than on strict applications of Bayesian methodology to forensic problems. Even though American administrations like the FBI or various police departments were involved. It was a highly coherent session and I had a pleasant discussion with some of the speakers after the session. For instance, my friend Alicia Carriquiry presented an approach to determined from images of bullets whether or not they have been fired from the same gun, leading to an interesting case for a point null hypothesis where the point null makes complete sense. The work has been published in Annals of Applied Statistics and is used in practice. The second talk by Danica Ommen on fiducial forensics on IED, asking whether or not copper wires used in the bombs are the same, which is another point null illustration. Which also set an interesting questioning on the dependence of the alternative prior on the distribution of material chosen as it is supposed to cover all possible origins for the disputed item. But more interestingly this talk launched into a discussion of making decision based on finite samplers and unknown parameters, not that specific to forensics, with a definitely surprising representation of the Bayes factor as an expected likelihood ratio which made me first reminiscent of Aitkin’s (1991) infamous posterior likelihood (!) before it dawned on me this was a form of bridge sampling identity where the likelihood ratio only involved parameters common to both models, making it an expression well-defined under both models. This identity could be generalised to the general case by considering a ratio of integrated likelihoods, the extreme case being the ratio equal to the Bayes factor itself. The following two talks by Larry Tang and Christopher Saunders were also focused on the likelihood ratio and their statistical estimates, debating the coherence of using a score function and presenting a functional ABC algorithm where the prior is a Dirichlet (functional) prior. Thus a definitely relevant session from a Bayesian perspective!


likelihood free nested sampling

Posted in Books, Statistics with tags , , , , , , , , , , , on April 26, 2019 by xi'an

A recent paper by Mikelson and Khammash found on bioRxiv considers the (paradoxical?) mixture of nested sampling and intractable likelihood. They however cover only the case when a particle filter or another unbiased estimator of the likelihood function can be found. Unless I am missing something in the paper, this seems a very costly and convoluted approach when pseudo-marginal MCMC is available. Or the rather substantial literature on computational approaches to state-space models. Furthermore simulating under the lower likelihood constraint gets even more intricate than for standard nested sampling as the parameter space is augmented with the likelihood estimator as an extra variable. And this makes a constrained simulation the harder, to the point that the paper need resort to a Dirichlet process Gaussian mixture approximation of the constrained density. It thus sounds quite an intricate approach to the problem. (For one of the realistic examples, the authors mention a 12 hour computation on a 48 core cluster. Producing an approximation of the evidence that is not unarguably stabilised, contrary to the above.) Once again, not being completely up-to-date in sequential Monte Carlo, I may miss a difficulty in analysing such models with other methods, but the proposal seems to be highly demanding with respect to the target.

statistics in Nature [a tale of the two Steves]

Posted in Books, pictures, Statistics with tags , , , , , , , , , on January 15, 2019 by xi'an

In the 29 November issue of Nature, Stephen Senn (formerly at Glasgow) wrote an article about the pitfalls of personalized medicine, for the statistics behind the reasoning are flawed.

“What I take issue with is the de facto assumption that the differential response to a drug is consistent for each individual, predictable and based on some stable property, such as a yet-to-be-discovered genetic variant.”S. Senn

One (striking) reason being that the studies rest on a sort of low-level determinism that does not account for many sources of variability. Over-confidence in causality results. Stephen argues that improvement lies in insisting on repeated experiments on the same subjects (with an increased challenge in modelling since this requires longitudinal models with dependent observations). And to “drop the use of dichotomies”, favouring instead continuous modeling of measurements.

And in the 6 December issue, Steven Goodman calls (in the World view tribune) for probability statements to be attached as confidence indices to scientific claims. That he takes great pain to distinguish from p-values and links with Bayesian analysis. (Bayesian analysis that Stephen regularly objects to.) While I applaud the call, I am quite pessimistic about the follow-up it will generate, the primary reply being that posterior probabilities can be manipulated as well as p-values. And that Bayesian probabilities are not “real” probabilities (dixit Don Fraser or Deborah Mayo).

nested sampling when prior and likelihood clash

Posted in Books, Statistics with tags , , , , , , , , , on April 3, 2018 by xi'an

A recent arXival by Chen, Hobson, Das, and Gelderblom makes the proposal of a new nested sampling implementation when prior and likelihood disagree, making simulations from the prior inefficient. The paper holds the position that a single given prior is used over and over all datasets that come along:

“…in applications where one wishes to perform analyses on many thousands (or even millions) of different datasets, since those (typically few) datasets for which the prior is unrepresentative can absorb a large fraction of the computational resources.” Chen et al., 2018

My reaction to this situation, provided (a) I want to implement nested sampling and (b) I realise there is a discrepancy, would be to resort to an importance sampling resolution, as we proposed in our Biometrika paper with Nicolas. Since one objection [from the authors] is that identifying outlier datasets is complicated (it should not be when the likelihood function can be computed) and time-consuming, sequential importance sampling could be implemented.

“The posterior repartitioning (PR) method takes advantage of the fact that nested sampling makes use of the likelihood L(θ) and prior π(θ) separately in its exploration of the parameter space, in contrast to Markov chain Monte Carlo (MCMC) sampling methods or genetic algorithms which typically deal solely in terms of the product.” Chen et al., 2018

The above salesman line does not ring a particularly convincing chime in that nested sampling is about as myopic as MCMC since based on the similar notion of a local proposal move, starting from the lowest likelihood argument (the minimum likelihood estimator!) in the nested sample.

“The advantage of this extension is that one can choose (π’,L’) so that simulating from π’ under the constraint L'(θ) > l is easier than simulating from π under the constraint L(θ) > l. For instance, one may choose an instrumental prior π’ such that Markov chain Monte Carlo steps adapted to the instrumental constrained prior are easier to implement than with respect to the actual constrained prior. In a similar vein, nested importance sampling facilitates contemplating several priors at once, as one may compute the evidence for each prior by producing the same nested sequence, based on the same pair (π’,L’), and by simply modifying the weight function.” Chopin & Robert, 2010

Since the authors propose to switch to a product (π’,L’) such that π’.L’=π.L, the solution appears like a special case of importance sampling, with the added drwaback that when π’ is not normalised, its normalised constant must be estimated as well. (With an extra nested sampling implementation?) Furthermore, the advocated solution is to use tempering, which is not so obvious as it seems in small dimensions. As the mass does not always diffuse to relevant parts of the space. A more “natural” tempering would be to use a subsample in the (sub)likelihood for nested sampling and keep the remainder of the sample for weighting the evaluation of the evidence.

Turing’s Bayesian contributions

Posted in Books, Kids, pictures, Running, Statistics, University life with tags , , , , , , , , , , , , on March 17, 2015 by xi'an

Following The Imitation Game, this recent movie about Alan Turing played by Benedict “Sherlock” Cumberbatch, been aired in French theatres, one of my colleagues in Dauphine asked me about the Bayesian contributions of Turing. I first tried to check in Sharon McGrayne‘s book, but realised it had vanished from my bookshelves, presumably lent to someone a while ago. (Please return it at your earliest convenience!) So I told him about the Bayesian principle of updating priors with data and prior probabilities with likelihood evidence in code detecting algorithms and ultimately machines at Bletchley Park… I could not got much farther than that and hence went checking on Internet for more fodder.

“Turing was one of the independent inventors of sequential analysis for which he naturally made use of the logarithm of the Bayes factor.” (p.393)

I came upon a few interesting entries but the most amazìng one was a 1979 note by I.J. Good (assistant of Turing during the War) published in Biometrika retracing the contributions of Alan Mathison Turing during the War. From those few pages, it emerges that Turing’s statistical ideas revolved around the Bayes factor that Turing used “without the qualification `Bayes’.” (p.393) He also introduced the notion of ban as a unit for the weight of evidence, in connection with the town of Banbury (UK) where specially formatted sheets of papers were printed “for carrying out an important classified process called Banburismus” (p.394). Which shows that even in 1979, Good did not dare to get into the details of Turing’s work during the War… And explains why he was testing simple statistical hypothesis against simple statistical hypothesis. Good also credits Turing for the expected weight of evidence, which is another name for the Kullback-Leibler divergence and for Shannon’s information, whom Turing would visit in the U.S. after the War. In the final sections of the note, Turing is also associated with Gini’s index, the estimation of the number of species (processed by Good from Turing’s suggestion in a 1953 Biometrika paper, that is, prior to Turing’s suicide. In fact, Good states in this paper that “a very large part of the credit for the present paper should be given to [Turing]”, p.237), and empirical Bayes.