## Easy computation of the Bayes Factor

Posted in Books, Statistics with tags , , , , , on August 21, 2021 by xi'an

“Choosing the ranges has been criticized as introducing subjectivity; however, the key point is that the ranges are given quantitatively and should be justified”

On arXiv, I came across a paper by physicists Dunstan, Crowne, and Drew, on computing the Bayes factor by linear regression. Paper that I found rather hard to read given that the method is never completely spelled out but rather described through some examples (or the captions of figures)… The magical formula (for the marginal likelihood)

$B=(2\pi)^{n/2}L_{\max}\dfrac{\text{Cov}_p}{\prod_{i=1}^n \Delta p_i}$

where n is the parameter dimension, Cov is the Fisher information matrix, and the denominator the volume of a flat prior on an hypercube (!), seems to come for a Laplace approximation. But it depends rather crucially (!) on the choice of this volume. A severe drawback the authors evacuate with the above quote… And by using an example where the parameters have a similar meaning under both models. The following ones compare several dimensions of parameters without justifying (enough) the support of the corresponding priors. In addition, using a flat prior over the hypercube seems to clash with the existence of a (Fisher) correlation between the components. (To be completely open as to why I discuss this paper, I was asked to review the paper, which I declined.)

## ABC on brain networks

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , on April 16, 2021 by xi'an

Research Gate sent me an automated email pointing out a recent paper citing some of our ABC papers. The paper is written by Timothy West et al., neuroscientists in the UK, comparing models of Parkinsonian circuit dynamics. Using SMC-ABC. One novelty is the update of the tolerance by a fixed difference, unless the acceptance rate is too low, in which case the tolerance is reinitialised to a starting value.

“(…) the proposal density P(θ|D⁰) is formed from the accepted parameters sets. We use a density approximation to the marginals and a copula for the joint (…) [i.e.] a nonparametric estimation of the marginal densities overeach parameter [and] the t-copula(…) Data are transformed to the copula scale (unit-square) using the kernel density estimator of the cumulative distribution function of each parameter and then transformed to the joint space with the t-copula.”

The construct of the proposal is quite involved, as described in the above quote. The model choice approach is standard (à la Grelaud et al.) but uses the median distance as a tolerance.

“(…) test whether the ABC estimator will: a) yield parameter estimates that are unique to the data from which they have been optimized; and b) yield consistent estimation of parameters across multiple instances (…) test the face validity of the model comparison framework (…) [and] demonstrate the scalability of the optimization and model comparison framework.”

The paper runs a fairly extensive test of the above features, concluding that “the ABC optimized posteriors are consistent across multiple initializations and that the output is determined by differences in the underlying model generating the given data.” Concerning model comparison, the authors mix the ABC Bayes factor with a post-hoc analysis of divergence to discriminate against overfitting. And mention the potential impact of the summary statistics in the conclusion section, albeit briefly, and the remark that the statistics were “sufficient to recover known parameters” is not supporting their use for model comparison. The additional criticism of sampling strategies for approximating Bayes factors is somewhat irrelevant, the main issue with ABC model choice being a change of magnitude in the evidence.

“ABC has established itself as a key tool for parameter estimation in systems biology (…) but is yet to see wide adoption in systems neuroscience. It is known that ABC will not perform well under certain conditions (Sunnåker et al., 2013). Specifically, it has been shown that the
simplest form of ABC algorithm based upon an rejection-sampling approach is inefficient in the case where the prior densities lie far from the true posterior (…) This motivates the use of neurobiologically grounded models over phenomenological models where often the ranges of potential parameter values are unknown.”

## Bayes factors revisited

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , on March 22, 2021 by xi'an

“Bayes factor analyses are highly sensitive to and crucially depend on prior assumptions about model parameters (…) Note that the dependency of Bayes factors on the prior goes beyond the dependency of the posterior on the prior. Importantly, for most interesting problems and models, Bayes factors cannot be computed analytically.”

Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner, Michael Betancourt, Shravan Vasishth have just arXived a massive document on the Bayes factor, worrying about the computation of this common tool, but also at the variability of decisions based on Bayes factors, e.g., stressing correctly that

“…we should not confuse inferences with decisions. Bayes factors provide inference on hypotheses. However, to obtain discrete decisions (…) from continuous inferences in a principled way requires utility functions. Common decision heuristics (e.g., using Bayes factor larger than 10 as a discovery threshold) do not provide a principled way to perform decisions, but are merely heuristic conventions.”

The text is long and at times meandering (at least in the sections I read), while trying a wee bit too hard to bring up the advantages of using Bayes factors versus frequentist or likelihood solutions. (The likelihood ratio being presented as a “frequentist” solution, which I think is an incorrect characterisation.) For instance, the starting point of preferring a model with a higher marginal likelihood is presented as an evidence (oops!) rather than argumented. Since this quantity depends on both the prior and the likelihood, it being high or low is impacted by both. One could then argue that using its numerical value as an absolute criterion amounts to selecting the prior a posteriori as much as checking the fit to the data! The paper also resorts to the Occam’s razor argument, which I wish we could omit, as it is a vague criterion, wide open to misappropriation. It is also qualitative, rather than quantitative, hence does not help in calibrating the Bayes factor.

Concerning the actual computation of the Bayes factor, an issue that has always been a concern and a research topic for me, the authors consider only two “very common methods”, the Savage–Dickey density ratio method and bridge sampling. We discussed the shortcomings of the Savage–Dickey density ratio method with Jean-Michel Marin about ten years ago. And while bridge sampling is an efficient approach when comparing models of the same dimension, I have reservations about this efficiency in other settings. Alternative approaches like importance nested sampling, noise contrasting estimation or SMC samplers are often performing quite efficiently as normalising constant approximations. (Not to mention our version of harmonic mean estimator with HPD support.)

Simulation-based inference is based on the notion that simulated data can be produced from the predictive distributions. Reminding me of ABC model choice to some extent. But I am uncertain this approach can be used to calibrate the decision procedure to select the most appropriate model. We thought about using this approach in our testing by mixture paper and it is favouring the more complex of the two models. This seems also to occur for the example behind Figure 5 in the paper.

Two other points: first, the paper does not consider the important issue with improper priors, which are not rigorously compatible with Bayes factors, as I discussed often in the past. And second, Bayes factors are not truly Bayesian decision procedures, since they remove the prior weights on the models, thus the mention of utility functions therein seems inappropriate unless a genuine utility function can be produced.

## 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!

## Bertrand-Borel debate

Posted in Books, Statistics with tags , , , , , , , , , , , , , on May 6, 2019 by xi'an

On her blog, Deborah Mayo briefly mentioned the Bertrand-Borel debate on the (in)feasibility of hypothesis testing, as reported [and translated] by Erich Lehmann. A first interesting feature is that both [starting with] B mathematicians discuss the probability of causes in the Bayesian spirit of Laplace. With Bertrand considering that the prior probabilities of the different causes are impossible to set and then moving all the way to dismiss the use of probability theory in this setting, nipping the p-values in the bud..! And Borel being rather vague about the solution probability theory has to provide. As stressed by Lehmann.

“The Pleiades appear closer to each other than one would naturally expect. This statement deserves thinking about; but when one wants to translate the phenomenon into numbers, the necessary ingredients are lacking. In order to make the vague idea of closeness more precise, should we look for the smallest circle that contains the group? the largest of the angular distances? the sum of squares of all the distances? the area of the spherical polygon of which some of the stars are the vertices and which contains the others in its interior? Each of these quantities is smaller for the group of the Pleiades than seems plausible. Which of them should provide the measure of implausibility? If three of the stars form an equilateral triangle, do we have to add this circumstance, which is certainly very unlikely apriori, to those that point to a cause?” Joseph Bertrand (p.166)

“But whatever objection one can raise from a logical point of view cannot prevent the preceding question from arising in many situations: the theory of probability cannot refuse to examine it and to give an answer; the precision of the response will naturally be limited by the lack of precision in the question; but to refuse to answer under the pretext that the answer cannot be absolutely precise, is to place oneself on purely abstract grounds and to misunderstand the essential nature of the application of mathematics.” Emile Borel (Chapter 4)

Another highly interesting objection of Bertrand is somewhat linked with his conditioning paradox, namely that the density of the observed unlikely event depends on the choice of the statistic that is used to calibrate the unlikeliness, which makes complete sense in that the information contained in each of these statistics and the resulting probability or likelihood differ to an arbitrary extend, that there are few cases (monotone likelihood ratio) where the choice can be made, and that Bayes factors share the same drawback if they do not condition upon the entire sample. In which case there is no selection of “circonstances remarquables”. Or of uniformly most powerful tests.