**T**here 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!

## Archive for OxWaSP

## PhD studenships at Warwick

Posted in Kids, pictures, Statistics, University life with tags Brexit, CDT, Centre for Doctoral Training in Mathematics and Statistics, computational statistics, European Union, mathematical statistics, OxWaSP, PhD fellowship, University of Warwick on May 2, 2019 by xi'an## distributed posteriors

Posted in Books, Statistics, Travel, University life with tags CDT, high dimensions, minimaxity, OxWaSP, parallel MCMC, scalable MCMC, statistical theory, University of Warwick on February 27, 2019 by xi'an**A**nother 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.)

## particular degeneracy in ABC model choice

Posted in Statistics with tags ABC, ABC-SMC, OxWaSP, project, sequential Monte Carlo, Storm Eric, University of Warwick on February 22, 2019 by xi'an**I**n one of the presentations by the last cohort of OxWaSP students, the group decided to implement an ABC model choice strategy based on sequential ABC inspired from Toni et al. (2008). and this made me reconsider this approach* (disclaimer: no criticism of the students implied in the following!)*. Indeed, the outcome of the simulation led to the ultimate selection of a single model, exclusive of all other models, corresponding to a posterior probability of one in favour of this model. Which sounds like a drawback of the ABC-SMC model choice approach in this setting, namely that it is quite prone to degeneracy, much more than standard SMC, since once a model vanishes from the list, it can never reappear in the following iterations if I am reading the algorithm correctly. To avoid this degeneracy, one would need to keep a population of particles of a given size, for each model, towards using it as a pool for moves at following iterations… Which also means that running in parallel as many ABC-SMC filters as there are models would be equally or more efficient, a wee bit like parallel MCMC chains may prove more efficient than reversible jump for model comparison. (On the trivial side, the OxWaSP seminar on the same day was briefly interrupted by water leakage caused by Storm Eric and poor workmanship on the new building!)

## warm stone & cold morning light [jatp]

Posted in pictures, Travel, University life with tags England, jatp, Jesus College, neo-gothic architecture, Oxford colleges, OxWaSP, University of Oxford on January 30, 2019 by xi'an## annual visit to Oxford

Posted in Kids, pictures, Statistics, Travel, University life with tags Avalon, Bayesian statistics, course, Oxford, OxWaSP, Roxy Music, segregation, St. Hugh's College, University of Oxford, University of Warwick on February 1, 2018 by xi'an**A**s in every year since 2014, I am spending a few days in Oxford to teach a module on Bayesian Statistics to our Oxford-Warwick PhD students. This time I was a wee bit under the weather due to a mild case of food poisoning and I can only hope that my more than sedate delivery did not turn definitely the students away from Bayesian pursuits!

The above picture is at St. Hugh’s College, where I was staying. Or should it be Saint Hughes, since this 12th century bishop was a pre-Brexit European worker from Avalon, France… (This college was created in 1886 for young women of poorer background. And only opened to male students a century later. The 1924 rules posted in one corridor show how these women were considered to be so “dangerous” by the institution that they had to be kept segregated from men, except their brothers!, at all times…)

## oxwasp@amazon.de

Posted in Books, Kids, pictures, Running, Statistics, Travel, University life with tags Amazon, Berlin, bier, Brauhaus Lemke, doubly intractable problems, Germany, Google, Ising model, machine learning, normalising constant, optimisation, OxWaSP, quantum computers, Spree, Stadtmitte, University of Oxford, University of Warwick, workshop on April 12, 2017 by xi'an**T**he reason for my short visit to Berlin last week was an OxWaSP (Oxford and Warwick Statistics Program) workshop hosted by Amazon Berlin with talks between statistics and machine learning, plus posters from our second year students. While the workshop was quite intense, I enjoyed very much the atmosphere and the variety of talks there. (Just sorry that I left too early to enjoy the social programme at a local brewery, Brauhaus Lemke, and the natural history museum. But still managed nice runs east and west!) One thing I found most interesting (if obvious in retrospect) was the different focus of academic and production talks, where the later do not aim at a full generality or at a guaranteed improvement over the existing, provided the new methodology provides a gain in efficiency over the existing.

This connected nicely with me reading several Nature articles on quantum computing during that trip, where researchers from Google predict commercial products appearing in the coming five years, even though the technology is far from perfect and the outcome qubit error prone. Among the examples they provided, quantum simulation (not meaning what I consider to be *simulation*!), quantum optimisation (as a way to overcome multimodality), and quantum sampling (targeting given probability distributions). I find the inclusion of the latest puzzling in that simulation (in that sense) shows very little tolerance for errors, especially systematic bias. It may be that specific quantum architectures can be designed for specific probability distributions, just like some are already conceived for optimisation. (It may even be the case that quantum solutions are (just next to) available for intractable constants as in Ising or Potts models!)