Archive for support

CIRM in dire need of help

Posted in Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , on November 24, 2020 by xi'an

The International Mathematics Conference Centre, CIRM, located in Luminy, Marseille, France, has been deeply disrupted by the COVID-19 pandemic. Up to 60 % of the events planned there have been cancelled so far and all remaining conferences have gone virtual or at best hybrid, open to a remote audience, including in an interactive way. This has required important and unexpected investments. Furthermore, this crisis happened at the worst possible moment as CIRM had just finished doubling its infrastructure capacities. This is why I strongly support the call from the French Mathematical Society (SMF) to make a donation to CIRM before the end of the year, especially from researchers who benefited from its hospitality and unique environment.

support Wikipedia

Posted in Books, University life with tags , , on March 15, 2020 by xi'an

support red X

Posted in Kids with tags , , , , on December 24, 2018 by xi'an

BNP12

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , 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.

prior against truth!

Posted in Books, Kids, Statistics with tags , , , , , , , on June 4, 2018 by xi'an

A question from X validated had interesting ramifications, about what happens when the prior does not cover the true value of the parameter (assuming there ? In fact, not so much in that, from a decision theoretic perspective, the fact that that π(θ⁰)=0, or even that π(θ)=0 in a neighbourhood of θ⁰ does not matter [too much]. Indeed, the formal derivation of a Bayes estimator as minimising the posterior loss means that the resulting estimator may take values that were “impossible” from a prior perspective! Indeed, taking for example the posterior mean, the convex combination of all possible values of θ under π may well escape the support of π when this support is not convex. Of course, one could argue that estimators should further be restricted to be possible values of θ under π but that would reduce their decision theoretic efficiency.

An example is the brilliant minimaxity result by George Casella and Bill Strawderman from 1981: when estimating a Normal mean μ based on a single observation xwith the additional constraint that |μ|<ρ, and when ρ is small enough, ρ1.0567 quite specifically, the minimax estimator for this problem under squared error loss corresponds to a (least favourable) uniform prior on the pair {ρ,ρ}, meaning that π gives equal weight to ρ and ρ (and none to any other value of the mean μ). When ρ increases above this bound, the least favourable prior sees its support growing one point at a time, but remaining a finite set of possible values. However the posterior expectation, 𝔼[μ|x], can take any value on (ρ,ρ).

In an even broader suspension of belief (in the prior), it may be that the prior has such a restricted support that it cannot consistently estimate the (true value of the) parameter, but the associated estimator may remain admissible or minimax.