Archive for the University life Category

Bayesian intelligence in Warwick

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , on February 18, 2019 by xi'an

This is an announcement for an exciting CRiSM Day in Warwick on 20 March 2019: with speakers

10:00-11:00 Xiao-Li Meng (Harvard): “Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox”

11:00-12:00 Julien Stoehr (Dauphine): “Gibbs sampling and ABC”

14:00-15:00 Arthur Ulysse Jacot-Guillarmod (École Polytechnique Fedérale de Lausanne): “Neural Tangent Kernel: Convergence and Generalization of Deep Neural Networks”

15:00-16:00 Antonietta Mira (Università della Svizzera italiana e Università degli studi dell’Insubria): “Bayesian identifications of the data intrinsic dimensions”

[whose abstracts are on the workshop webpage] and free attendance. The title for the workshop mentions Bayesian Intelligence: this obviously includes human intelligence and not just AI!

research outreach wants to improve my public image [ltd]

Posted in Books, University life with tags , , , , , on February 17, 2019 by xi'an

I have received this most bizarre email (links are mine):

Dear Dr. Christian Robert,

Please excuse the direct nature of this contact, however I would like to speak with you regarding your work on the Accelerating MCMC algorithms study.

Research Outreach work in collaboration with research teams assisting with their Public Outreach activity, through means of a professionally produced series of publications and articles aimed at a broader audience. I understand Public Outreach is a very important issue within the research community – where it is often difficult to obtain large numbers of views and interactions.

We are currently working on our April 2019 edition. This publication will feature a wide variety of science disciplines, including approximately 28 research articles. We would like you to be one of those featured. My suggestion is that we might create a new article, possibly covering some basic details of the published paper I have seen online, or indeed, covering new ground or the wider scope of your work. This would be an entirely new article written and developed by Research Outreach, in close collaboration with you.

Research Outreach distributes all content, publications and your individual article, across major Social Media platforms, as well as our own website. Your work will be seen by a large and quantifiable global audience. We are a non-profit community company, all content we produce is free to share and can be downloaded by any reader. Our website is entirely transparent and answers any questions you may have.

I have briefly looked over the details of your work and believe Research Outreach to be an effective platform to communicate your outreach requirements. We publish under the Creative Commons Licence and you will own the copyright to your published material, and we can send you a separate PDF download, to be used on your web-page, at events, conferences, or as outreach material for students etc.

Please have a look at our website for examples of the publications we produce and further detail on our objectives and services. I can assure you the entire process requires very little work on your part, less than 1 hour, over a 10-week process. Some of the services we provide are paid for, this means there is a cost to you if you decide to take part. I would be delighted to explain this and our editorial process. I understand your time is limited.

Rather than writing any further detail, could we please find 5 minutes to discuss? During our call, I can answer any questions you may have and explain in detail the requirements and cost for taking part and of course the benefits of doing so.

This sounds rather predatory to me, since I have to pay this company to produce a paper I (co?)authored, and of dubious academic worth if I spend less than one hour on the project. When looking at the issues on-line, the contents of the articles seem quite light, with glossy images not always related with the topic of the journal article. If not predatory, then a sort of advertising agency for academics?! Weird times…

 

undecidable learnability

Posted in Books, Statistics, Travel, University life with tags , , , , , , on February 15, 2019 by xi'an

“There is an unknown probability distribution P over some finite subset of the interval [0,1]. We get to see m i.i.d. samples from P for m of our choice. We then need to find a finite subset of [0,1] whose P-measure is at least 2/3. The theorem says that the standard axioms of mathematics cannot be used to prove that we can solve this problem, nor can they be used to prove that we cannot solve this problem.”

In the first issue of the (controversial) nature machine intelligence journal, Ben-David et al. wrote a paper they present a s the machine learning equivalent to Gödel’s incompleteness theorem. The result is somewhat surprising from my layman perspective and it seems to only relate to a formal representation of statistical problems. Formal as in the Vapnik-Chervonenkis (PAC) theory. It sounds like, given a finite learning dataset, there are always features that cannot be learned if the size of the population grows to infinity, but this is hardly exciting…

The above quote actually makes me think of the Robbins-Wasserman counter-example for censored data and Bayesian tail prediction, but I am unsure the connection is anything more than sheer fantasy..!
~

O’Bayes 19: registration and travel support

Posted in pictures, Running, Travel, University life with tags , , , , , , , on February 14, 2019 by xi'an

An update about the O’Bayes 19 conference next June-July:  the early registration period has now opened. And there should be funds for supporting early-career researchers, thanks to Google and CNRS sponsorships, as detailed below:

Early-career researchers less than four years from PhD, are invited to apply for early-career scholarships. If you are a graduate student, postdoctoral researcher or early-career academic and you are giving a poster, you are eligible to apply. Female researchers and underrepresented minorities are especially encouraged to apply. Selected applicants will receive up to £450, which can be used for any combination of fees, travel and accommodation costs, subject to receipts.

The deadline for applying is the 15th of March (which is also the deadline to submit the abstract for the poster) and it has to be done at the registration phase via the dedicated page. Those who have submitted an abstract before this information on scholarships was made available (11 Feb.) and applying for travel support should contact the organisers.

a pen for ABC

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , on February 13, 2019 by xi'an

Among the flury of papers arXived around the ICML 2019 deadline, I read on my way back from Oxford a paper by Wiqvist et al. on learning summary statistics for ABC by neural nets. Pointing out at another recent paper by Jiang et al. (2017, Statistica Sinica) which constructed a neural network for predicting each component of the parameter vector based on the input (raw) data, as an automated non-parametric regression of sorts. Creel (2017) does the same but with summary statistics. The current paper builds up from Jiang et al. (2017), by adding the constraint that exchangeability and partial exchangeability features should be reflected by the neural net prediction function. With applications to Markovian models. Due to a factorisation theorem for d-block invariant models, the authors impose partial exchangeability for order d Markov models by combining two neural networks that end up satisfying this factorisation. The concept is exemplified for one-dimension g-and-k distributions, alpha-stable distributions, both of which are made of independent observations, and the AR(2) and MA(2) models, as in our 2012 ABC survey paper. Since the later is not Markovian the authors experiment with different orders and reach the conclusion that an order of 10 is most appropriate, although this may be impacted by being a ble to handle the true likelihood.

Jeffreys priors for hypothesis testing [Bayesian reads #2]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , on February 9, 2019 by xi'an

A second (re)visit to a reference paper I gave to my OxWaSP students for the last round of this CDT joint program. Indeed, this may be my first complete read of Susie Bayarri and Gonzalo Garcia-Donato 2008 Series B paper, inspired by Jeffreys’, Zellner’s and Siow’s proposals in the Normal case. (Disclaimer: I was not the JRSS B editor for this paper.) Which I saw as a talk at the O’Bayes 2009 meeting in Phillie.

The paper aims at constructing formal rules for objective proper priors in testing embedded hypotheses, in the spirit of Jeffreys’ Theory of Probability “hidden gem” (Chapter 3). The proposal is based on symmetrised versions of the Kullback-Leibler divergence κ between null and alternative used in a transform like an inverse power of 1+κ. With a power large enough to make the prior proper. Eventually multiplied by a reference measure (i.e., the arbitrary choice of a dominating measure.) Can be generalised to any intrinsic loss (not to be confused with an intrinsic prior à la Berger and Pericchi!). Approximately Cauchy or Student’s t by a Taylor expansion. To be compared with Jeffreys’ original prior equal to the derivative of the atan transform of the root divergence (!). A delicate calibration by an effective sample size, lacking a general definition.

At the start the authors rightly insist on having the nuisance parameter v to differ for each model but… as we all often do they relapse back to having the “same ν” in both models for integrability reasons. Nuisance parameters make the definition of the divergence prior somewhat harder. Or somewhat arbitrary. Indeed, as in reference prior settings, the authors work first conditional on the nuisance then use a prior on ν that may be improper by the “same” argument. (Although conditioning is not the proper term if the marginal prior on ν is improper.)

The paper also contains an interesting case of the translated Exponential, where the prior is L¹ Student’s t with 2 degrees of freedom. And another one of mixture models albeit in the simple case of a location parameter on one component only.

revisiting marginalisation paradoxes [Bayesian reads #1]

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on February 8, 2019 by xi'an

As a reading suggestion for my (last) OxWaSP Bayesian course at Oxford, I included the classic 1973 Marginalisation paradoxes by Phil Dawid, Mervyn Stone [whom I met when visiting UCL in 1992 since he was sharing an office with my friend Costas Goutis], and Jim Zidek. Paper that also appears in my (recent) slides as an exercise. And has been discussed many times on this  ‘Og.

Reading the paper in the train to Oxford was quite pleasant, with a few discoveries like an interesting pike at Fraser’s structural (crypto-fiducial?!) distributions that “do not need Bayesian improper priors to fall into the same paradoxes”. And a most fascinating if surprising inclusion of the Box-Müller random generator in an argument, something of a precursor to perfect sampling (?). And a clear declaration that (right-Haar) invariant priors are at the source of the resolution of the paradox. With a much less clear notion of “un-Bayesian priors” as those leading to a paradox. Especially when the authors exhibit a red herring where the paradox cannot disappear, no matter what the prior is. Rich discussion (with none of the current 400 word length constraint), including the suggestion of neutral points, namely those that do identify a posterior, whatever that means. Funny conclusion, as well:

“In Stone and Dawid’s Biometrika paper, B1 promised never to use improper priors again. That resolution was short-lived and let us hope that these two blinkered Bayesians will find a way out of their present confusion and make another comeback.” D.J. Bartholomew (LSE)

and another

“An eminent Oxford statistician with decidedly mathematical inclinations once remarked to me that he was in favour of Bayesian theory because it made statisticians learn about Haar measure.” A.D. McLaren (Glasgow)

and yet another

“The fundamentals of statistical inference lie beneath a sea of mathematics and scientific opinion that is polluted with red herrings, not all spawned by Bayesians of course.” G.N. Wilkinson (Rothamsted Station)

Lindley’s discussion is more serious if not unkind. Dennis Lindley essentially follows the lead of the authors to conclude that “improper priors must go”. To the point of retracting what was written in his book! Although concluding about the consequences for standard statistics, since they allow for admissible procedures that are associated with improper priors. If the later must go, the former must go as well!!! (A bit of sophistry involved in this argument…) Efron’s point is more constructive in this regard since he recalls the dangers of using proper priors with huge variance. And the little hope one can hold about having a prior that is uninformative in every dimension. (A point much more blatantly expressed by Dickey mocking “magic unique prior distributions”.) And Dempster points out even more clearly that the fundamental difficulty with these paradoxes is that the prior marginal does not exist. Don Fraser may be the most brutal discussant of all, stating that the paradoxes are not new and that “the conclusions are erroneous or unfounded”. Also complaining about Lindley’s review of his book [suggesting prior integration could save the day] in Biometrika, where he was not allowed a rejoinder. It reflects on the then intense opposition between Bayesians and fiducialist Fisherians. (Funny enough, given the place of these marginalisation paradoxes in his book, I was mistakenly convinced that Jaynes was one of the discussants of this historical paper. He is mentioned in the reply by the authors.)