Easy computation of the Bayes Factor

“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)

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.)

can you spare a dime? [or rather 113,900?]

Just read the announcement in Nature of 24 November that

Publisher Springer Nature has announced how scientists can make their papers in its most selective titles free to read as soon as they are published.

which is presented as a great advance to make scientific papers available for all to read. The catch is that there is no free lunch, obviously, as the author(s) have to pay Springer a 1,514,324.68 krónur charge for immediate open access! The Nature article does mention the issue obviously, as this is such a huge amount of money that it makes publishing under such conditions inaccessible for all academics but those with sufficient funding grants. It also mentions an alternate scheme contemplated by some Nature outlets to introduce “a non-refundable fee of €2,190 to cover an editorial assessment and the peer-review process.” None of the fee going to reviewers, apparently. This “evolution” (?!) is driven by the EU Plan S for making scientific publications available to all, but it even more crucially calls for a radical reassessment of publishing policies for research that is publicly funded and publicly reviewed, then paid again by publicly funded libraries and institutions. Even more radical than India’s push for `One nation, one subscription’.

news from PCI

Nature Outlook on AI

The 29 November 2018 issue of Nature had a series of papers on AIs (in its Outlook section). At the general public (awareness) level than in-depth machine-learning article. Including one on the forecasted consequences of ever-growing automation on jobs, quoting from a 2013 paper by Carl Frey and Michael Osborne [of probabilistic numerics fame!] that up to 47% of US jobs could become automated. The paper is inconclusive on how taxations could help in or deter from transfering jobs to other branches, although mentioning the cascading effect of taxing labour and subsidizing capital. Another article covers the progresses in digital government, with Estonia as a role model, including the risks of hacking (but not mentioning Russia’s state driven attacks). Differential privacy is discussed as a way to keep data “secure” (but not cryptography à la Louis Aslett!). With another surprising entry that COBOL is still in use in some administrative systems. Followed by a paper on the apparently limited impact of digital technologies on mental health, despite the advertising efforts of big tech companies being described as a “race to the bottom of the brain stem”! And another one on (overblown) public expectations on AIs, although the New York Time had an entry yesterday on people in Arizona attacking self-driving cars with stones and pipes… Plus a paper on the growing difficulties of saving online documents and culture for the future (although saving all tweets ever published does not sound like a major priority to me!).

Interesting (?) aside, the same issue contains a general public article on the use of AIs for peer reviews (of submitted papers). The claim being that “peer review by artificial intelligence (AI) is promising to improve the process, boost the quality of published papers — and save reviewers time.” A wee bit over-optimistic, I would say, as the developed AI’s are at best “that statistics and methods in manuscripts are sound”. For instance, producing “key concepts to summarize what the paper is about” is not particularly useful. A degree of innovation compared with the existing would be. Or an automated way to adapt the paper style to the strict and somewhat elusive Biometrika style!

a good start in Series B!

Just received the great news for the turn of the year that our paper on ABC using Wasserstein distance was accepted in Series B! Inference in generative models using the Wasserstein distance, written by Espen Bernton, Pierre Jacob, Mathieu Gerber, and myself, bypasses the (nasty) selection of summary statistics in ABC by considering the Wasserstein distance between observed and simulated samples. It focuses in particular on non-iid cases like time series in what I find fairly innovative ways. I am thus very glad the paper is going to appear in JRSS B, as it has methodological consequences that should appeal to the community at large.