**F**irst up Dennis Prangle presented his recent work on “Lazy ABC”, which can speed up ABC by potentially abandoning model simulations early that do not look promising. Dennis introduces a continuation probability to ensure that the target distribution of the approach is still the ABC target of interest. In effect, the ABC likelihood is estimated to be 0 if early stopping is performed otherwise the usual ABC likelihood is inflated by dividing by the continuation probability, ensuring an unbiased estimator of the ABC likelihood. The drawback is that the ESS (Dennis uses importance sampling) of the lazy approach will likely be less than usual ABC for a fixed number of simulations; but this should be offset by the reduction in time required to perform said simulations. Dennis also presented some theoretical work for optimally tuning the method, which I need more time to digest.

**T**his was followed by my talk on Bayesian indirect inference methods that use a parametric auxiliary model (a slightly older version here). This paper has just been accepted by Statistical Science.

**M**orning tea was followed by my PhD student, Brenda Vo, who presented an interesting application of ABC to cell spreading experiments. Here an estimate of the diameter of the cell population was used as a summary statistic. It was noted after Brenda’s talk that this application might be a good candidate for Dennis’ Lazy ABC idea. This talk was followed by a much more theoretical presentation by Pierre del Moral on how particle filter methodologies can be adapted to the ABC setting and also a general framework for particle methods.

**F**ollowing lunch, Guilherme Rodrigues presented a hierarchical Gaussian Process model for kernel density estimation in the presence of different subgroups. Unfortunately my (lack of) knowledge on non-parametric methods prevents me from making any further comment except that the model looked very interesting and ABC seemed a good candidate for calibrating the model. I look forward to the paper appearing on-line.

**T**he next presentation was by Gael Martin who spoke about her research on using ABC for estimation of complex state space models. This was probably my favourite talk of the day, and not only because it is very close to my research interests. Here the score of the Euler discretised approximation of the generative model was used as summary statistics for ABC. From what I could gather, it was demonstrated that the ABC posterior based on the score or the MLE of the auxiliary model were the same in the limit as ε 0 (unless I have mis-interpreted). This is a very useful result in itself; using the score to avoid an optimisation required for the MLE can save a lot of computation. The improved approximations of the proposed approach compared with the results that use the likelihood of the Euler discretisation were quite promising. I am certainly looking forward to this paper coming out.

**M**att Moores drew the short straw and had the final presentation on the Friday afternoon. Matt spoke about this paper (an older version is available here), of which I am now a co-author. Matt’s idea is that doing some pre-simulations across the prior space and determining a mapping between the parameter of interest and the mean and variance of the summary statistic can significantly speed up ABC for the Potts model, and potentially other ABC applications. The results of the pre-computation step are used in the main ABC algorithm, which no longer requires simulation of pseudo-data but rather a summary statistic can be simulated from the fitted auxiliary model in the pre-processing step. Whilst this approach does introduce a couple more layers of approximation, the gain in computation time was up to two orders of magnitude. The talks by Matt, Gael and myself gave a real indirect inference flavour to this year’s ABC in…

Filed under: pictures, Statistics, University life Tagged: abc-in-sydney, Australia, Chris Drovandi, Sydney ]]>

**S**witching between a scalable computation session with Alex Beskos, who talked about adaptive Langevin algorithms for differential equations, and a non-local prior session, with David Rossell presenting a smoother way to handle point masses in order to accommodate frequentist coverage. Something we definitely need to discuss the next time I am in Warwick! Although this made me alas miss both the first talk of the non-local session by Shane Jensen the final talk of the scalable session by Doug Vandewrken where I happened to be quoted (!) for my warning about discretising Markov chains into non-Markov processes. In the 1998 JASA paper with Chantal Guihenneuc.

**A**fter a farewell meal of ceviche with friends in the sweltering humidity of a local restaurant, I attended [the newly elected ISBA Fellow!] Maria Vanucci’s talk on her deeply involved modelling of fMRI. The last talk before the airport shuttle was François Caron’s description of a joint work with Emily Fox on a sparser modelling of networks, along with an auxiliary variable approach that allowed for parallelisation of a Gibbs sampler. François mentioned an earlier alternative found in machine learning where all components of a vector are updated simultaneously conditional on the previous avatar of the other components, e.g. simulating (x’,y’) from π(x’|y) π(y’|x) which does not produce a convergent Markov chain. At least not convergent to the right stationary. However, running a quick [in-flight] check on a 2-d normal target did not show any divergent feature, when compared with the regular Gibbs sampler. I thus wonder at what can be said about the resulting target or which conditions are need for divergence. A few scribbles later, I realised that the 2-d case was the exception, namely that the stationary distribution of the chain is the product of the marginal. However, running a 3-d example with an auto-exponential distribution in the taxi back home, I still could not spot a difference in the outcome.

Filed under: pictures, Statistics, Travel, University life Tagged: Cancún, ISBA, Langevin MCMC algorithm, MCMC algorithms, non-local priors, University of Warwick ]]>

Filed under: pictures, Statistics, Travel, University life Tagged: Aero Mexico, Cancún, flight, ISBA 2014, Maya, Mexico, poster session ]]>

Filed under: Kids, Wines Tagged: California, Californian wine, Gallo vineyards, zinfandel ]]>

- don’t drink heavily

- don’t party or make noise

- don’t host visitors, day or night

- don’t bang the front door or leave the balcony door open when opening the front door

- don’t put cans or bottles on top of the glass cooktop

- don’t cook elaborate meals

- don’t try to fit an entire chicken in the oven

- don’t spill oil or wine on the kitchentop

- don’t cut food directly on the kitchentop

- don’t eat or drink while in bed

- avoid frying, curry, and bacon

- shop for groceries only one day at a time

- hot water may or may not be available

- elevator may or may not be available

- don’t bring sand back in the condo

Filed under: pictures, Travel Tagged: beach, Cancún, condo, flat, Mexico, rental ]]>

**H**alf-day #2 indeed at ISBA 2014, as the Wednesday afternoon kept to the Valencia tradition of free time, and potential cultural excursions, so there were only talks in the morning. And still the core poster session at (late) night. In which my student Kaniav Kamari presented a poster on a current project we are running with Kerrie Mengersen and Judith Rousseau on the replacement of the standard Bayesian testing setting with a mixture representation. Being half-asleep by the time the session started, I did not stay long enough to collect data on the reactions to this proposal, but the paper should be arXived pretty soon. And Kate Lee gave a poster on our importance sampler for evidence approximation in mixtures (soon to be revised!). There was also an interesting poster about reparameterisation towards higher efficiency of MCMC algorithms, intersecting with my long-going interest in the matter, although I cannot find a mention of it in the abstracts. And I had a nice talk with Eduardo Gutierrez-Pena about infering on credible intervals through loss functions. There were also a couple of appealing posters on g-priors. Except I was sleepwalking by the time I spotted them… (My conference sleeping pattern does not work that well for ISBA meetings! Thankfully, both next editions will be in Europe.)

**G**reat talk by Steve McEachern that linked to our ABC work on Bayesian model choice with insufficient statistics, arguing towards robustification of Bayesian inference by only using summary statistics. Despite this being “against the hubris of Bayes”… Obviously, the talk just gave a flavour of Steve’s perspective on that topic and I hope I can read more to see how we agree (or not!) on this notion of using insufficient summaries to conduct inference rather than trying to model “the whole world”, given the mistrust we must preserve about models and likelihoods. And another great talk by Ioanna Manolopoulou on another of my pet topics, capture-recapture, although she phrased it as a partly identified model (as in Kline’s talk yesterday). This related with capture-recapture in that when estimating a capture-recapture model with covariates, sampling and inference are biased as well. I appreciated particularly the use of BART to analyse the bias in the modelling. And the talk provided a nice counterpoint to the rather pessimistic approach of Kline’s.

**T**errific plenary sessions as well, from Wilke’s spatio-temporal models (in the spirit of his superb book with Noel Cressie) to Igor Prunster’s great entry on Gibbs process priors. With the highly significant conclusion that those processes are best suited for (in the sense that they are only consistent for) discrete support distributions. Alternatives are to be used for continuous support distributions, the special case of a Dirichlet prior constituting a sort of unique counter-example. Quite an inspiring talk (even though I had a few micro-naps throughout it!).

**I** shared my afternoon free time between discussing the next O’Bayes meeting (2015 is getting very close!) with friends from the Objective Bayes section, getting a quick look at the Museo Maya de Cancún (terrific building!), and getting some work done (thanks to the lack of wireless…)

Filed under: pictures, Running, Statistics, Travel, University life Tagged: ABC, Bayesian tests, beach, Cancún, g-priors, ISBA 2014, Maya, Mexico, mixture estimation, O-Bayes 2015, posters, sunrise, Valencia conferences ]]>

**I**n the first of those sessions, Brendan Kline talked about partially identified parameters, a topic quite close to my interests, although I did not buy the overall modelling adopted in the analysis. For instance, Brendan Kline presented the example of a parameter θ that is the expectation of a random variable Y which is indirectly observed through __x__ <Y< x̅ . While he maintained that inference should be restricted to an interval around θ and that using a prior on θ was doomed to fail (and against econometrics culture), I would have prefered to see this example as a missing data one, with both __x__ and x̅ containing information about θ. And somewhat object to the argument against the prior as it would equally apply to any prior modelling. Although unrelated in the themes, Angela Bitto presented a work on the impact of different prior modellings on the estimation of time-varying parameters in time-series models. À la Harrison and West 1994 Discriminating between good and poor shrinkage in a way I could not spot. Unless it was based on the data fit (horror!). And a third talk of interest by Andriy Norets that (very loosely) related to Angela’s talk by presenting a framework to modify credible sets towards frequentist properties: one example was the credible interval on a positive normal mean that led to a frequency-valid confidence interval with a modified prior. This reminded me very much of the shrinkage confidence intervals of the James-Stein era.

Filed under: pictures, Statistics, Travel, University life Tagged: Bayesian statistics, Cancún, econometrics, genomics, ISBA 2004, Mexico, poster, shrinkage estimation ]]>
*[Scott Sisson sent me this summary of the ABC in Sydney meeting that took place two weeks ago.]*

**F**ollowing on from ABC in Paris (2009), ABC in London (2011) and ABC in Rome (2013), the fourth instalment of the international workshops in Approximate Bayesian Computation (ABC) was held at UNSW in Sydney on 3rd-4th July 2014. The first antipodean workshop was held as a satellite to the huge (>550 registrations) IMS-ASC-2014 International Conference, also held in Sydney the following week.

ABC in Sydney was created in two parts. The first, on the Thursday, was held as an “introduction to ABC” for people who were interested to find out more about the subject, but who had not particularly been exposed to the area before. Rather than have a single brave individual give the introductory course over several hours, the expository presentation was “crowdsourced” from several experienced researchers in the field, with each being given 30 minutes to present on a particular aspect of ABC. In this way, Matthew Moores (QUT), Dennis Prangle (Reading), Chris Drovandi (QUT), Zach Aandahl (UNSW) and Scott Sisson (UNSW) covered the ABC basics over the course of 6 presentations and 3 hours.

**T**he second part of the workshop, on Friday, was the more usual collection of research oriented talks. In the morning session, Dennis Prangle spoke about “lazy ABC,” a method of stopping the generation of computationally demanding dataset simulations early, and Chris Drovandi discussed theoretical and practical aspects of Bayesian indirect inference. This was followed by Brenda Nho Vo (QUT) presenting an application of ABC in stochastic cell spreading models, and by Pierre Del Moral (UNSW) who demonstrated many theoretical aspects of ABC in interacting particle systems. After lunch Guilherme Rodrigues (UNSW) proposed using ABC for Gaussian process density estimation (and introduced the infinite-dimensional functional regression adjustment), and Gael Martin (Monash) spoke on the issues involved in applying ABC to state space models. The final talk of the day was given by Matthew Moores who discussed how online ABC dataset generation could be circumvented by pre-computation for particular classes of models.

**I**n all, over 100 people registered for and attended the workshop, making it an outstanding success. Of course, this was helped by the association with the following large conference, and the pricing scheme — completely free! — following the tradition of the previous workshops. Morning and afternoon teas, described as “the best workshop food ever!” by several attendees, was paid for by the workshop sponsors: the Bayesian Section of the Statistical Society of Australia, and the ARC Centre of Excellence in Mathematical and Statistical Frontiers.

**H**ere’s looking forward to the next workshop in the series!

Filed under: pictures, Statistics, University life Tagged: abc-in-sydney, Australia, Scott Sisson, Sydney ]]>

**W**aiting for a Spanish speaking friend to kindly drive with me downtown Cancún to check whether or not an optician could make me new prescription glasses, I attended Jim Berger’s foundational lecture on frequentist properties of Bayesian procedures but could only listen as the slides were impossible for me to read, with or without glasses. The partial overlap with the Varanasi lecture helped. I alas had to skip both Gareth Roberts’ and Sylvia Früwirth-Schnatter’s lectures, apologies to both of them!, but the reward was to get a new pair of prescription glasses within a few hours. Perfectly suited to my vision! And to get back just in time to read slides during Peter Müller’s lecture from the back row! Thanks to my friend Sophie for her negotiating skills! Actually, I am still amazed at getting glasses that quickly, given the time it would have taken in, e.g., France. All set for another 15 years with the same pair?! Only if I do not go swimming with them in anything but a quiet swimming pool!

**T**he starting dinner happened to coincide with the (second) ISBA Fellow Award ceremony. Jim acted as the grand master of ceremony and he did great to add life and side stories to the written nominations for each and everyone of the new Fellows. The Fellowships honoured Bayesian statisticians who had contributed to the field as researchers and to the society since its creation. I thus feel very honoured (and absolutely undeserving) to be included in this prestigious list, along with many friends. (But would have loved to see two more former ISBA presidents included, esp. for their massive contribution to Bayesian theory and methodology…) And also glad to wear regular glasses instead of my morning sunglasses.

*[My Internet connection during the meeting being abysmally poor, the posts will appear with some major delay! In particular, I cannot include new pictures at times I get a connection... Hence a picture of northern Finland instead of Cancún at the top of this post!]*

Filed under: Statistics, Travel, University life Tagged: ABC, Cancún, Caribean sea, ISBA, Jim Berger, Mexico, short course, sunglasses, Valencia conferences ]]>

where F is the target. This distance (times √n) has an asymptotic distribution that does not depend on n, called the Kolmogorov distribution. After searching for a little while, we could not figure where this distribution was available in R. It had to, since ks.test was returning a p-value. Hopefully correct! So I looked into the ks.test function, which happens not to be entirely programmed in C, and found the line

PVAL <- 1 - if (alternative == "two.sided") .Call(C_pKolmogorov2x, STATISTIC, n)

which means that the Kolmogorov distribution is coded as a C function C_pKolmogorov2x in R. However, I could not call the function myself.

> .Call(C_pKolmogorov2x,.3,4) Error: object 'C_pKolmogorov2x' not found

Hence, as I did not want to recode this distribution cdf, I posted the question on stackoverflow (long time no see!) and got a reply almost immediately as to use the package kolmim. Followed by the extra comment from the same person that calling the C code only required to add the path to its name, as in

> .Call(stats:::C_pKolmogorov2x,STAT=.3,n=4) [1] 0.2292

Filed under: Books, Kids, R, Statistics, University life Tagged: C code, importance sampling, Introducing Monte Carlo Methods with R, kolmim, Kolmogorov-Smirnov distance, R, stackoverflow, Université Paris Dauphine ]]>