Archive for University of Warwick

O’Bayes 2019 conference program

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

PhD studenships at Warwick

Posted in Kids, pictures, Statistics, University life with tags , , , , , , , , on May 2, 2019 by xi'an

There is an exciting opening for several PhD positions at Warwick, in the departments of Statistics and of Mathematics, as part of the Centre for Doctoral Training in Mathematics and Statistics newly created by the University. CDT studentships are funded for four years and funding is open to students from the European Union without restrictions. (No Brexit!) Funding includes a stipend at UK/RI rates and tuition fees at UK/EU rates. Applications are made via the University of Warwick Online Application Portal and should be made  as quickly as possible since the funding will be allocated on a first come first serve basis. For more details, contact the CDT director, Martyn Plummer. I cannot but strongly encourage interested students to apply as this is a great opportunity to start a research career in a fantastic department!

Stein’s method in machine learning [workshop]

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , on April 5, 2019 by xi'an

There will be an ICML workshop on Stein’s method in machine learning & statistics, next July 14 or 15, located in Long Beach, CA. Organised by François-Xavier Briol (formerly Warwick), Lester Mckey, Chris Oates (formerly Warwick), Qiang Liu, and Larry Golstein. To quote from the webpage of the workshop

Stein’s method is a technique from probability theory for bounding the distance between probability measures using differential and difference operators. Although the method was initially designed as a technique for proving central limit theorems, it has recently caught the attention of the machine learning (ML) community and has been used for a variety of practical tasks. Recent applications include goodness-of-fit testing, generative modeling, global non-convex optimisation, variational inference, de novo sampling, constructing powerful control variates for Monte Carlo variance reduction, and measuring the quality of Markov chain Monte Carlo algorithms.

Speakers include Anima Anandkumar, Lawrence Carin, Louis Chen, Andrew Duncan, Arthur Gretton, and Susan Holmes. I am quite sorry to miss two workshops dedicated to Stein’s work in a row, the other one being at NUS, Singapore, around the Stein paradox.

O’Bayes 2019, more posters if you please!

Posted in pictures, Statistics, Travel, University life with tags , , , , , , on March 26, 2019 by xi'an

As the announcement for O’Bayes 2019 appeared in the March issue of the IMS Bulletin I just received, let me call for further poster submissions to the meeting. The deadline for travel support submission is past, but poster submissions are welcomed till June 15. (Overlap with BNP12 is not an issue!) Submissions should be sent to me (gmail address: bayesianstatistics) as a one page pdf file. Registration is open, with accommodation options.

a new rule for adaptive importance sampling

Posted in Books, Statistics with tags , , , , , , , , , on March 5, 2019 by xi'an

Art Owen and Yi Zhou have arXived a short paper on the combination of importance sampling estimators. Which connects somehow with the talk about multiple estimators I gave at ESM last year in Helsinki. And our earlier AMIS combination. The paper however makes two important assumptions to reach optimal weighting, which is inversely proportional to the variance:

  1. the estimators are uncorrelated if dependent;
  2. the variance of the k-th estimator is of order a (negative) power of k.

The later is puzzling when considering a series of estimators, in that k appears to act as a sample size (as in AMIS), the power is usually unknown but also there is no reason for the power to be the same for all estimators. The authors propose to use ½ as the default, both because this is the standard Monte Carlo rate and because the loss in variance is then minimal, being 12% larger.

As an aside, Art Owen also wrote an invited discussion “the unreasonable effectiveness of Monte Carlo” of ” Probabilistic Integration: A Role in Statistical Computation?” by François-Xavier Briol, Chris  Oates, Mark Girolami (Warwick), Michael Osborne and Deni Sejdinovic, to appear in Statistical Science, discussion that contains a wealth of smart and enlightening remarks. Like the analogy between pseudo-random number generators [which work unreasonably well!] vs true random numbers and Bayesian numerical integration versus non-random functions. Or the role of advanced bootstrapping when assessing the variability of Monte Carlo estimates (citing a paper of his from 1992). Also pointing out at an intriguing MCMC paper by  Michael Lavine and Jim Hodges to appear in The American Statistician.

distributed posteriors

Posted in Books, Statistics, Travel, University life with tags , , , , , , , on February 27, 2019 by xi'an

Another presentation by our OxWaSP students introduced me to the notion of distributed posteriors, following a 2018 paper by Botond Szabó and Harry van Zanten. Which corresponds to the construction of posteriors when conducting a divide & conquer strategy. The authors show that an adaptation of the prior to the division of the sample is necessary to recover the (minimax) convergence rate obtained in the non-distributed case. This is somewhat annoying, except that the adaptation amounts to take the original prior to the power 1/m, when m is the number of divisions. They further show that when the regularity (parameter) of the model is unknown, the optimal rate cannot be recovered unless stronger assumptions are made on the non-zero parameters of the model.

“First of all, we show that depending on the communication budget, it might be advantageous to group local machines and let different groups work on different aspects of the high-dimensional object of interest. Secondly, we show that it is possible to have adaptation in communication restricted distributed settings, i.e. to have data-driven tuning that automatically achieves the correct bias-variance trade-off.”

I find the paper of considerable interest for scalable MCMC methods, even though the setting may happen to sound too formal, because the study incorporates parallel computing constraints. (Although I did not investigate the more theoretical aspects of the paper.)

Tile Hill [paintings]

Posted in Statistics with tags , , , , , , , , , , , on February 24, 2019 by xi'an