The O’Bayes 2019 conference in Warwick University has now started, with about 100 participants meeting over four days (plus one of tutorials) in the Zeeman maths building of the University. Quite a change of location and weather when compared with the previous one in Austin. As an organiser I hope all goes well at the practical level and want to thank the other persons who helped me towards this goal, first and foremost Paula Matthews who solved web and lodging and planning issues all over these past months, as well as Mark Steel and Cristiano Villa. As a member of the scientific committee, I am looking forward the talks and discussants along the coming four days, again hoping all speakers and discussants show up and are not hindered by travel or visa issues…
Archive for objective Bayes
O’Bayes 2019 has now started!
Posted in pictures, Running, Statistics, Travel, University life with tags Coventry, Great-Britain, mathematics department, O'Bayes 2019, objective Bayes, subjective versus objective Bayes, summer of British conferences, University of Warwick, Warwickshire, Zeeman building on June 28, 2019 by xi'anO’Bayes 2019 conference program
Posted in Kids, pictures, Statistics, Travel, University life with tags Bayesian conference, Bayesian model selection, BNP12, England, frequentist inference, imprecise probabilities, ISBA, O'Bayes 2019, objective Bayes, prior selection, Statistical learning, University of Warwick on May 13, 2019 by xi'an
The full and definitive program of the O’Bayes 2019 conference in Warwick is now on line. Including discussants for all papers. And the three [and free] tutorials on Friday afternoon, 28 June, on model selection (M. Barbieri), MCMC recent advances (G.O. Roberts) and BART (E.I. George). 
Registration remains open at the reduced rate and submissions of posters can still be sent to me for all conference participants.
O’Bayes 2019: speakers, discussants, posters!
Posted in pictures, Statistics, University life with tags conference, O'Bayes 2019, objective Bayes, poster session, University of Warwick on January 2, 2019 by xi'an
The program for the next O’Bayes conference in Warwick, 28 June-02 July, 2019, is now set. Speakers and discussants have been contacted by the scientific committee and accepted our invitation! As usual, there will be poster sessions on the nights of 29 and 30 June and the call is open for poster submissions, until January 31, to be sent to me as a one page pdf document containing either the poster itself or title, abstract and references. (Using my email address at either Dauphine or Warwick is fine. Or bayesianstatistics on gmail.)
noninformative Bayesian prior with a finite support
Posted in Statistics, University life with tags Bayesian nonparametrics, data dependent priors, minimal description length principle, minimaxity, noninformative priors, objective Bayes, PNAS on December 4, 2018 by xi'an
A few days ago, Pierre Jacob pointed me to a PNAS paper published earlier this year on a form of noninformative Bayesian analysis by Henri Mattingly and coauthors. They consider a prior that “maximizes the mutual information between parameters and predictions”, which sounds very much like José Bernardo’s notion of reference priors. With the rather strange twist of having the prior depending on the data size m even they work under an iid assumption. Here information is defined as the difference between the entropy of the prior and the conditional entropy which is not precisely defined in the paper but looks like the expected [in the data x] Kullback-Leibler divergence between prior and posterior. (I have general issues with the paper in that I often find it hard to read for a lack of precision and of definition of the main notions.)
One highly specific (and puzzling to me) feature of the proposed priors is that they are supported by a finite number of atoms, which reminds me very much of the (minimax) least favourable priors over compact parameter spaces, as for instance in the iconic paper by Casella and Strawderman (1984). For the same mathematical reason that non-constant analytic functions must have separated maxima. This is conducted under the assumption and restriction of a compact parameter space, which must be chosen in most cases. somewhat arbitrarily and not without consequences. I can somehow relate to the notion that a finite support prior translates the limited precision in the estimation brought by a finite sample. In other words, given a sample size of m, there is a maximal precision one can hope for, producing further decimals being silly. Still, the fact that the support of the prior is fixed a priori, completely independently of the data, is both unavoidable (for the prior to be prior!) and very dependent on the choice of the compact set. I would certainly prefer to see a maximal degree of precision expressed a posteriori, meaning that the support would then depend on the data. And handling finite support posteriors is rather awkward in that many notions like confidence intervals do not make much sense in that setup. (Similarly, one could argue that Bayesian non-parametric procedures lead to estimates with a finite number of support points but these are determined based on the data, not a priori.)
Interestingly, the derivation of the “optimal” prior is operated by iterations where the next prior is the renormalised version of the current prior times the exponentiated Kullback-Leibler divergence, which is “guaranteed to converge to the global maximum” for a discretised parameter space. The authors acknowledge that the resolution is poorly suited to multidimensional settings and hence to complex models, and indeed the paper only covers a few toy examples of moderate and even humble dimensions.
Another difficulty with the paper is the absence of temporal consistency: since the prior depends on the sample size, the posterior for n i.i.d. observations is no longer the prior for the (n+1)th observation.
“Because it weights the irrelevant parameter volume, the Jeffreys prior has strong dependence on microscopic effects invisible to experiment”
I simply do not understand the above sentence that apparently counts as a criticism of Jeffreys (1939). And would appreciate anyone enlightening me! The paper goes into comparing priors through Bayes factors, which ignores the main difficulty of an automated solution such as Jeffreys priors in its inability to handle infinite parameter spaces by being almost invariably improper.
BNP12
Posted in pictures, Statistics, Travel, University life with tags Bayesian nonparametrics, BNP12, Coventry, England, ISBA, Midlands, O'Bayes 2019, objective Bayes, Oxford, support, UK, University of Oxford, University of Warwick on October 9, 2018 by xi'an
The next BNP (Bayesian nonparametric) conference is taking place in Oxford (UK), prior to the O’Bayes 2019 conference in Warwick, in June 24-28 and June 29-July 2, respectively. At this stage, the Scientific Committee of BNP12 invites submissions for possible contributed talks. The deadline for submitting a title/abstract is 15th December 2018. And the submission of applications for travel support closes on 15th December 2018. Currently, there are 35 awards that could be either travel awards or accommodation awards. The support is for junior researchers (students currently enrolled in a Dphil (PhD) programme or having graduated after 1st October 2015). The applicant agrees to present her/his work at the conference as a poster or oraly if awarded the travel support.
As for O’Bayes 2019, we are currently composing the programme, following the 20 years tradition of these O’Bayes meetings of having the Scientific Committee (Marilena Barbieri, Ed George, Brunero Liseo, Luis Pericchi, Judith Rousseau and myself) inviting about 25 speakers to present their recent work and 25 discussants to… discuss these works. With a first day of introductory tutorials to Bayes, O’Bayes and beyond. I (successfully) proposed this date and location to the O’Bayes board to take advantage of the nonparametric Bayes community present in the vicinity so that they could attend both meetings at limited cost and carbon impact.
