Archive for OxWaSP

Savage Award session today at JSM

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , , , , on August 3, 2020 by xi'an

Pleased to broadcast the JSM session dedicated to the 2020 Savage Award, taking place today at 13:00 ET (17:00 GMT), with two of the Savage nominees being former OxWaSP students (and Warwick PhD students). For those who have not registered for JSM, the talks are also available on Bayeslab. (As it happens, I was also a member of the committee this year, but do not think this could be deemed a CoI!)

112 Mon, 8/3/2020, 1:00 PM – 2:50 PM Virtual
Savage Award Session — Invited Papers
International Society for Bayesian Analysis (ISBA)
Organizer(s): Maria De Iorio, University College London
Chair(s): Maria De Iorio, University College London
1:05 PM Bayesian Dynamic Modeling and Forecasting of Count Time Series
Lindsay Berry, Berry Consultants
1:30 PM Machine Learning Using Approximate Inference: Variational and Sequential Monte Carlo Methods
Christian Andersson Naesseth, Columbia University
1:55 PM Recent Advances in Bayesian Probabilistic Numerical Integration
Francois-Xavier Briol, University College London
2:20 PM Factor regression for dimensionality reduction and data integration techniques with applications to cancer data
Alejandra Avalos Pacheco, Harvard Medical School
2:45 PM Floor Discussion

JB³ [Junior Bayes beyond the borders]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , on June 22, 2020 by xi'an

Bocconi and j-ISBA are launcing a webinar series for and by junior Bayesian researchers. The first talk is on 25 June, 25 at 3pm UTC/GMT (5pm CET) with Francois-Xavier Briol, one of the laureates of the 2020 Savage Thesis Prize (and a former graduate of OxWaSP, the Oxford-Warwick doctoral training program), on Stein’s method for Bayesian computation, with as a discussant Nicolas Chopin.

As pointed out on their webpage,

Due to the importance of the above endeavor, JB³ will continue after the health emergency as an annual series. It will include various refinements aimed at increasing the involvement of the whole junior Bayesian community and facilitating a broader participation to the online seminars all over the world via various online solutions.

Thanks to all my friends at Bocconi for running this experiment!

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!

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.)

particular degeneracy in ABC model choice

Posted in Statistics with tags , , , , , , on February 22, 2019 by xi'an

In 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!)