Archive for University of Oxford

snapshot from Oxford [#2]

Posted in Kids, pictures, Travel, University life with tags , , , , on February 9, 2016 by xi'an

Oxford snapshot

Posted in pictures, Running, Travel, University life with tags , , , on February 2, 2016 by xi'an

off to Oxford

Posted in Kids, pictures, Travel, University life with tags , , , , , , , on January 31, 2016 by xi'an

Oxford, Feb. 23, 2012I am off to Oxford this evening for teaching once again in the Bayesian module of the OxWaSP programme. Joint PhD programme between Oxford and Warwick, supported by the EPSRC. And with around a dozen new [excellent!] PhD students every year. Here are the slides of a longer course that I will use in the coming days:

And by popular request (!) here is the heading of my Beamer file:

% Rather be using my own color
\setbeamercolor{alerted text}{fg=lightred}

métro static

Posted in Kids, Statistics, Travel with tags , , , , , , on July 19, 2015 by xi'an

[In the train shuttle at Birmingham airport, two young guys, maybe back from SPA 2015, discussing signal processing:]

– In Bayesian statistics, they use a different approach to testing hypotheses… You see, they put priors on the different hypotheses…

– But in the end it all boils down to concentration inequalities…

Oxford snapshot

Posted in pictures, Travel, University life with tags , , , , , on July 18, 2015 by xi'an


SPA 2015 Oxford [my day #2]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , on July 17, 2015 by xi'an

KebleToday I [barely made it on a delayed train from Leaminton Spa to Oxford as I] chaired my invited session at SPA 2015 on advanced MCMC methodology. The three speakers, Randal Douc, Mike Pitt and Matti Vihola, all gave talks related to the pseudo-marginal technique. For instance, Randal gave examples of guaranteed variance improvements by adding randomisation steps in the generation of the rv’s behind the unbiased estimation of the likelihood function. Mike Pitt presented the paper I discussed a little while ago about evaluating the computing performances of pseudo-marginal approximations, with a fairly compelling perspective [I may have missed from the paper] on approximating the distribution on the approximation to the log-likelihood as a normal. Which led me to ponder at the ultimate version where the log-likelihood itself would get directly simulated in an MCMC algorithm bypassing the preliminary simulation of the parameters. Sounds a bit too fantasy-like to be of any use… Matti Vihola also presented recent results with Christophe Andrieu on comparing pseudo-marginal approximations, based on convex ordering properties. They included a domination result on ABC-MCM algorithms, as noted in a recent post. Which made me musing about the overall importance of unbiasedness in the global picture, where all we need are converging approximations, in fine.

SPA 2015 Oxford

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , on July 14, 2015 by xi'an

Today I gave a talk on Approximate Bayesian model choice via random forests at the yearly SPA (Stochastic Processes and their Applications) 2015 conference, taking place in Oxford (a nice town near Warwick) this year. In Keble College more precisely. The slides are below and while they are mostly repetitions of earlier slides, there is a not inconsequential novelty in the presentation, namely that I included our most recent and current perspective on ABC model choice. Indeed, when travelling to Montpellier two weeks ago, we realised that there was a way to solve our posterior probability conundrum!

campusDespite the heat wave that rolled all over France that week, we indeed figured out a way to estimate the posterior probability of the selected (MAP) model, way that we had deemed beyond our reach in previous versions of the talk and of the paper. The fact that we could not provide an estimate of this posterior probability and had to rely instead on a posterior expected loss was one of the arguments used by the PNAS reviewers in rejecting the paper. While the posterior expected loss remains a quantity worth approximating and reporting, the idea that stemmed from meeting together in Montpellier is that (i) the posterior probability of the MAP is actually related to another posterior loss, when conditioning on the observed summary statistics and (ii) this loss can be itself estimated via a random forest, since it is another function of the summary statistics. A posteriori, this sounds trivial but we had to have a new look at the problem to realise that using ABC samples was not the only way to produce an estimate of the posterior probability! (We are now working on the revision of the paper for resubmission within a few week… Hopefully before JSM!)


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