Archive for i-like

guess what..?! Yet another worskhop in the endless summer Bayesian series!

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on April 18, 2018 by xi'an

Dennis Prangle pointed to me the perfectly timed i-like workshop taking place in Newcastle, on the days priors to ABC in Edinburgh and ISBA (similarly in Edinburgh!). (Note that Warwick is also part of the i-like network. Actually, the first i-like workshop was my first trip abroad after the Accident!) I may sound negative about these workshops, but on the opposite am quite a fan of them, just regretting that the main event did not take advantage of them all to reduce the volume of talks there. As I suggested, it could have been feasible to label these satellites as part of the main conference towards making speakers at these officially speakers at ISBA 2018 in case talks were required for support…

The i-like workshop 2018 is the sixth edition of a yearly series of workshops dedicated to the topic of intractable likelihoods, hosted by Newcastle University. The workshop will take place from Wednesday 20 June 2018 – Friday 22 June 2018 in Room 2.98, Armstrong Building, Newcastle upon Tyne. Registration is free and mandatory!

I spent a few days in Newcastle at the RSS meeting of 2013, with my friends Jim Hobert and Elias Moreno. Enjoying very much the city, its surroundings, the great meadow north of the city in a glorious sunset (I still bemoan not catching on camera!). And it is just in the vicinity of Hadrian’s Wall, just on the other side of the Borders, very close to Edinburgh in fact.

lazy ABC

Posted in Books, Statistics, University life with tags , , , , , , , on June 9, 2014 by xi'an

“A more automated approach would be useful for lazy versions of ABC SMC algorithms.”

Dennis Prangle just arXived the work on lazy ABC he had presented in Oxford at the i-like workshop a few weeks ago. The idea behind the paper is to cut down massively on the generation of pseudo-samples that are “too far” from the observed sample. This is formalised through a stopping rule that puts the estimated likelihood to zero with a probability 1-α(θ,x) and otherwise divide the original ABC estimate by α(θ,x). Which makes the modification unbiased when compared with basic ABC. The efficiency appears when α(θ,x) can be computed much faster than producing the entire pseudo-sample and its distance to the observed sample. When considering an approximation to the asymptotic variance of this modification, Dennis derives a optimal (in the sense of the effective sample size) if formal version of the acceptance probability α(θ,x), conditional on the choice of a “decision statistic” φ(θ,x).  And of an importance function g(θ). (I do not get his Remark 1 about the case when π(θ)/g(θ) only depends on φ(θ,x), since the later also depends on x. Unless one considers a multivariate φ which contains π(θ)/g(θ) itself as a component.) This approach requires to estimate


as a function of φ: I would have thought (non-parametric) logistic regression a good candidate towards this estimation, but Dennis is rather critical of this solution.

I added the quote above as I find it somewhat ironical: at this stage, to enjoy laziness, the algorithm has first to go through a massive calibration stage, from the selection of the subsample [to be simulated before computing the acceptance probability α(θ,x)] to the construction of the (somewhat mysterious) decision statistic φ(θ,x) to the estimation of the terms composing the optimal α(θ,x). The most natural choice of φ(θ,x) seems to be involving subsampling, still with a wide range of possibilities and ensuing efficiencies. (The choice found in the application is somehow anticlimactic in this respect.) In most ABC applications, I would suggest using a quick & dirty approximation of the distribution of the summary statistic.

A slight point of perplexity about this “lazy” proposal, namely the static role of ε, which is impractical because not set in stone… As discussed several times here, the tolerance is a function of many factors incl. all the calibration parameters of the lazy ABC, rather than an absolute quantity. The paper is rather terse on this issue (see Section 4.2.2). It seems to me that playing with a large collection of tolerances may be too costly in this setting.

¼th i-like workshop in St. Anne’s College, Oxford

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , on March 27, 2014 by xi'an

IMG_0153Due to my previous travelling to and from Nottingham for the seminar and back home early enough to avoid the dreary evening trains from Roissy airport (no luck there, even at 8pm, the RER train was not operating efficiently!, and no fast lane is planed prior to 2023…), I did not see many talks at the i-like workshop. About ¼th, roughly… I even missed the poster session (and the most attractive title of Lazy ABC by Dennis Prangle) thanks to another dreary train ride from Derby to Oxford.

IMG_0150As it happened I had already heard or read parts of the talks in the Friday morning session, but this made understanding them better. As in Banff, Paul Fearnhead‘s talk on reparameterisations for pMCMC on hidden Markov models opened a wide door to possible experiments on those algorithms. The examples in the talk were mostly of the parameter duplication type, somewhat creating unidentifiability to decrease correlation, but I also wondered at the possibility of introducing frequent replicas of the hidden chain in order to fight degeneracy. Then Sumeet Singh gave a talk on the convergence properties of noisy ABC for approximate MLE. Although I had read some of the papers behind the talk, it made me realise how keeping balls around each observation in the ABC acceptance step was not leading to extinction as the number of observations increased. (Summet also had a good line with his ABCDE algorithm, standing for ABC done exactly!) Anthony Lee covered his joint work with Krys Łatuszyński on the ergodicity conditions on the ABC-MCMC algorithm, the only positive case being the 1-hit algorithm as discussed in an earlier post. This result will hopefully get more publicity, as I frequently read that increasing the number of pseudo-samples has no clear impact on the ABC approximation. Krys Łatuszyński concluded the morning with an aggregate of the various results he and his co-authors had obtained on the fascinating Bernoulli factory. Including constructive derivations.

After a few discussions on and around research topics, it was too soon time to take advantage of the grand finale of a March shower to walk from St. Anne’s College to Oxford Station, in order to start the trip back home. I was lucky enough to find a seat and could start experimenting in R the new idea my trip to Nottingham had raised! While discussing a wee bit with my neighbour, a delightful old lady from the New Forest travelling to Coventry, recovering from a brain seizure, wondering about my LaTeX code syntax despite the tiny fonts, and who most suddenly popped a small screen from her bag to start playing Candy Crush!, apologizing all the same. The overall trip was just long enough for my R code to validate this idea of mine, making this week in England quite a profitable one!!! IMG_0145

i-like Oxford [workshop, March 20-21, 2014]

Posted in Statistics, Travel, University life with tags , , , , , on February 5, 2014 by xi'an

There will be another i-like workshop this Spring, over two days in Oxford, St Anne’s College, involving talks by Xiao-Li Meng and Eric Moulines, as well as by researchers from the participating universities. Registration is now open. (I will take part as a part-time participant, travelling from Nottingham where I give a seminar on the 20th.)

thumbleweed [local] news

Posted in Books, Kids, Mountains, pictures, Running, Travel, University life, Wines with tags , , , , , , , , , on May 26, 2013 by xi'an

As a few more weeks have gone since I left the hospital, here are some news for the aficionadi (apulgaradi?). The wound on the thumb is  healing at a good pace, although the dressings are still on for one or two weeks. While I am still recovering from those weeks in the hospital, lacking energy at times (and getting quickly tired by metro rides), the only major after-effect is an intolerance to beer. Hopefully temporary! I managed to get back to an almost daily run in the nearby park (and to lose my cameraagain!, in the process). Once again, most sincere thanks to all of you who sent and keep sending me greetings and good  wishes, incl. special thanks to my friends in the Statistics department at QUT for their collective postcard [and yes they can laugh about ït]! And to friends from New York who called me several times. Although my scientific production is very limited at the moment, since the i-like workshop was both enjoyable and cathartic, I plan to attend the French statistical meeting next week in Toulouse [hopefully getting some kg back from the great South-West cuisine!], followed by ABC in Roma [another opportunity for great food]. On the following weekend, I should leave for Vietnam to give a course on Bayesian analysis and attend a conference as well.

i-like[d the] workshop

Posted in Running, Statistics, Travel, University life with tags , , , , , , , , on May 17, 2013 by xi'an

Indeed, I liked the i-like workshop very much. Among the many interesting talks of the past two days (incl. Cristiano Varin’s ranking of Series B as the top influential stat. journal!) , Matti Vihola’s and Nicolas Chopin’s had the strongest impact on me (to the point of scribbling in my notebook). In a joint work with Christophe Andrieu, Matti focussed on evaluating the impact of replacing the target with an unbiased estimate in a Metropolis-Hastings algorithm. In particular, they found necessary and sufficient conditions for keeping geometric and uniform ergodicity. My question (asked by Iain Murray) was whether they had derived ways of selecting the number of terms in the unbiased estimator towards maximal efficiency. I also wonder if optimal reparameterisations can be found in this sense (since unbiased estimators remain unbiased after reparameterisation).

Nicolas’ talk was about particle Gibbs sampling, a joint paper with Sumeet Singh recently arXived. I did not catch the whole detail of their method but/as I got intrigued by a property of Marc Beaumont’s algorithm (the very same algorithm used by Matti & Christophe). Indeed, the notion is that an unbiased estimator of the target distribution can be found in missing variable settings by picking an importance sampling distribution q on those variables. This representation leads to a pseudo-target Metropolis-Hastings algorithm. In the stationary regime, there exists a way to derive an “exact” simulation from the joint posterior on (parameter,latent). All the remaining/rejected latents are then distributed from the proposal q. What I do not see is how this impacts the next MCMC move since it implies generating a new sample of latent variables. I spoke with Nicolas about this over breakfast: the explanation is that this re-generated set of latent variables can be used in the denominator of the Metropolis-Hastings acceptance probability and is validated as a Gibbs step. (Incidentally, it may be seen as a regeneration event as well.)

bike trail from Kenilworth to the University of WarwickFurthermore, I had a terrific run in the rising sun (at 5am) all the way to Kenilworth where I saw a deer, pheasants and plenty of rabbits. (As well as this sculpture that now appears to me as being a wee sexist…)

Warwickshire snapshot

Posted in pictures, Running, Travel with tags , , on May 17, 2013 by xi'an

Westwood church