phishing alert at CIRM!

A strong and loud warning to all participants to one of the three events organised in CIRM, Luminy, Marseilles, in conjunction with Kerrie Mengersen’s Jean Morlet visiting chair, namely that some participants have received calls from crooks posting as CIRM admins, asking for credit card details towards supporting their stay at CIRM. This is a phishing attempt as self-supported participants to these events will be asked to pay at the end of their stay and never by phone or mail. In the meanwhile, there remains a few entries for both

1. Bayesian Statistics in the Big Data Era (26-30 Nov, 2018)
   
2. Young Bayesians and Big Data for Social Good (23-26 Nov., 2018)
   

for which registration is free but compulsory.

IMS workshop [day 3]

I made the “capital” mistake of walking across the entire NUS campus this morning, which is quite green and pretty, but which almost enjoys an additional dimension brought by such an intense humidity that one feels having to get around this humidity!, a feature I have managed to completely erase from my memory of my previous visit there. Anyway, nothing of any relevance. oNE talk in the morning was by Markus Eisenbach on tools used by physicists to speed up Monte Carlo methods, like the Wang-Landau flat histogram, towards computing the partition function, or the distribution of the energy levels, definitely addressing issues close to my interest, but somewhat beyond my reach for using a different language and stress, as often in physics. (I mean, as often in physics talks I attend.) An idea that came out clear to me was to bypass a (flat) histogram target and aim directly at a constant slope cdf for the energy levels. (But got scared away by the Fourier transforms!)

Lawrence Murray then discussed some features of the Birch probabilistic programming language he is currently developing, especially a fairly fascinating concept of delayed sampling, which connects with locally-optimal proposals and Rao Blackwellisation. Which I plan to get back to later [and hopefully sooner than later!].

In the afternoon, Maria de Iorio gave a talk about the construction of nonparametric priors that create dependence between a sequence of functions, a notion I had not thought of before, with an array of possibilities when using the stick breaking construction of Dirichlet processes.

And Christophe Andrieu gave a very smooth and helpful entry to partly deterministic Markov processes (PDMP) in preparation for talks he is giving next week for the continuation of the workshop at IMS. Starting with the guided random walk of Gustafson (1998), which extended a bit later into the non-reversible paper of Diaconis, Holmes, and Neal (2000). Although I had a vague idea of the contents of these papers, the role of the velocity ν became much clearer. And premonitory of the advances made by the more recent PDMP proposals. There is obviously a continuation with the equally pedagogical talk Christophe gave at MCqMC in Rennes two months [and half the globe] ago,  but the focus being somewhat different, it really felt like a new talk [my short term memory may also play some role in this feeling!, as I now remember the discussion of Hilderbrand (2002) for non-reversible processes]. An introduction to the topic I would recommend to anyone interested in this new branch of Monte Carlo simulation! To be followed by the most recently arXived hypocoercivity paper by Christophe and co-authors.

IMS workshop [day 2]

Here are the slides of my talk today on using Wasserstein distances as an intrinsic distance measure in ABC, as developed in our papers with Espen Bernton, Pierre Jacob, and Mathieu Gerber:

This morning, Gael Martin discussed the surprising aspects of ABC prediction, expanding upon her talk at ISBA, with several threads very much worth weaving in the ABC tapestry, one being that summary statistics need be used to increase the efficiency of the prediction, as well as more adapted measures of distance. Her talk also led me ponder about the myriad of possibilities available or not in the most generic of ABC predictions (which is not the framework of Gael’s talk). If we imagine a highly intractable setting, it may be that the marginal generation of a predicted value at time t+1 requires the generation of the entire past from time 1 till time t. Possibly because of a massive dependence on latent variables. And the absence of particle filters. if this makes any sense. Therefore, based on a generated parameter value θ it may be that the entire series needs be simulated to reach the last value in the series. Even when unnecessary this may be an alternative to conditioning upon the actual series. In this later case, comparing both predictions may act as a natural measure of distance since one prediction is a function or statistic of the actual data while the other is a function of the simulated data. Another direction I mused about is the use of (handy) auxiliary models, each producing a prediction as a new statistic, which could then be merged and weighted (or even selected) by a random forest procedure. Again, if the auxiliary models are relatively well-behaved, timewise, this would be quite straightforward to implement.

Larry Brown memorial and workshop, Penn, Nov 30-Dec 01

There will be a memorial service and a workshop in memory of Larry Brown next November 30 – December 01 at Penn, Philadelphia, with talks by Jim Berger, Peter Bickel, Andreas Buja, Tony Cai, Ian Johnstone, Michael Jordan, Mark Low, Avi Mandelbaum, and Bin Yu. Unfortunately for me, this celebration clashes with our “big Bayes” conference in CIRM, Marseilles which ends up on the 30th, so I will not be able to take part in this opportunity to remember and celebrate Larry.

off to Singapore [IMS workshop]

Tonight I am off to the National University of Singapore, at the Institute for Mathematical Sciences [and not the Institute of Mathematical Statistics!], to take part in a (first) week workshop on Bayesian Computation for High-Dimensional Statistical Models, covering topics like Approximate Bayesian Computation, Markov chain Monte Carlo, Multilevel Monte Carlo and Particle Filters. Having just barely recovered from the time difference with Vancouver, I now hope I can switch with not too much difficulty to Singapore time zone! As well as face the twenty plus temperature gap with the cool weather this morning in the Parc…

advances in Bayesian modelling a Venezia

ABC in Ed’burgh

A glorious day for this new edition of the “ABC in…” workshops, in the capital City of Edinburgh! I enjoyed very much this ABC day for demonstrating ABC is still alive and kicking!, i.e., enjoying plenty of new developments and reinterpretations. With more talks and posters on the way during the main ISBA 2018 meeting. (All nine talks are available on the webpage of the conference.)

After Michael Gutmann’s tutorial on ABC, Gael Martin (Monash) presented her recent work with David Frazier, Ole Maneesoonthorn, and Brendan McCabe on ABC  for prediction. Maybe unsurprisingly, Bayesian consistency for the given summary statistics is a sufficient condition for concentration of the ABC predictor, but ABC seems to do better for the prediction problem than for parameter estimation, not losing to exact Bayesian inference, possibly because in essence the summary statistics there need not be of a large dimension to being consistent. The following talk by Guillaume Kon Kam King was also about prediction, for the specific problem of gas offer, with a latent Wright-Fisher point process in the model. He used a population ABC solution to handle this model.

Alexander Buchholz (CREST) introduced an ABC approach with quasi-Monte Carlo steps that helps in reducing the variability and hence improves the approximation in ABC. He also looked at a Negative Geometric variant of regular ABC by running a random number of proposals until reaching a given number of acceptances, which while being more costly produces more stability.

Other talks by Trevelyan McKinley, Marko Järvenpää, Matt Moores (Warwick), and Chris Drovandi (QUT) illustrated the urge of substitute models as a first step, and not solely via Gaussian processes. With for instance the new notion of a loss function to evaluate this approximation. Chris made a case in favour of synthetic vs ABC approaches, due to degradation of the performances of nonparametric density estimation with the dimension. But I remain a doubting Thomas [Bayes] on that point as high dimensions in the data or the summary statistics are not necessarily the issue, as also processed in the paper on ABC-CDE discussed on a recent post. While synthetic likelihood requires estimating a mean function and a covariance function of the parameter of the dimension of the summary statistic. Even though estimated by simulation.

Another neat feature of the day was a special session on cosmostatistics with talks by Emille Ishida and Jessica Cisewski, from explaining how ABC was starting to make an impact on cosmo- and astro-statistics, to the special example of the stellar initial mass distribution in clusters.

Call is now open for the next “ABC in”! Note that, while these workshops have been often formally sponsored by ISBA and its BayesComp section, they are not managed by a society or a board of administrators, and hence are not much contrived by a specific format. It would just be nice to keep the low fees as part of the tradition.