Archive for Chamonix

next BayesComp conference planned for Jan 2018, any volunteer?

Posted in Kids, Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on February 25, 2016 by xi'an

MCMSki III poster, 2010 (C.) IMS[A call from the BayesComp section of ISBA for the next Bayesian computation meeting! As suggested in an earlier post, the label MCMski is discontinued to allow for any location amenable to organise a 200 plus meeting in good and hopefully reasonably priced conditions.]

The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship meeting: BayesComp 2018

The expectation is that the meeting will be held in January 2018, but the committee will consider proposals for other times through January 2019. This meeting is a continuation of the popular MCMSki on recent advances in the theory and application of Bayesian computational methods such as MCMC. The tradition was to hold MCMski meetings in ski resorts, but, as the name change suggests, we encourage applications from any venue that could support BC2018.

A three-day meeting is planned, perhaps with an additional day or two of satellite meetings and/or short courses. One page proposals should address feasibility of hosting the meeting including

1. Proposed dates.
2. Transportation for international participants (both the proximity of international airports and transportation to/from the venue).
3. The conference facilities.
4. The availability and cost of hotels, including low cost options.
5. The proposed local organizing committee and their collective experience organizing international meetings.
6. Expected or promised contributions from the host organization, host country, or industrial partners towards the cost of running the meetings.

Proposals should be submitted to Nicolas Chopin (Program Chair) no later than May 31, 2016. The Board of Bayesian Computing Section will evaluate the proposals, choose a venue, and appoint the Program Committee for BayesComp 2018.

expectation-propagation from Les Houches

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , , , , , , on February 3, 2016 by xi'an

ridge6As CHANCE book editor, I received the other day from Oxford University Press acts from an École de Physique des Houches on Statistical Physics, Optimisation, Inference, and Message-Passing Algorithms that took place there in September 30 – October 11, 2013.  While it is mostly unrelated with Statistics, and since Igor Caron already reviewed the book a year and more ago, I skimmed through the few chapters connected to my interest, from Devavrat Shah’s chapter on graphical models and belief propagation, to Andrea Montanari‘s denoising and sparse regression, including LASSO, and only read in some detail Manfred Opper’s expectation propagation chapter. This paper made me realise (or re-realise as I had presumably forgotten an earlier explanation!) that expectation propagation can be seen as a sort of variational approximation that produces by a sequence of iterations the distribution within a certain parametric (exponential) family that is the closest to the distribution of interest. By writing the Kullback-Leibler divergence the opposite way from the usual variational approximation, the solution equates the expectation of the natural sufficient statistic under both models… Another interesting aspect of this chapter is the connection with estimating normalising constants. (I noticed a slight typo on p.269 in the final form of the Kullback approximation q() to p().

MCMskv, Lenzerheide, 4-7 Jan., 2016 [news #1]

Posted in Kids, Mountains, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , on July 20, 2015 by xi'an

moonriseThe BayesComp MCMski V [or MCMskv for short] has now its official website, once again maintained by Merrill Lietchy from Drexel University, Philadelphia, and registration is even open! The call for contributed sessions is now over, while the call for posters remains open until the very end. The novelty from the previous post is that there will be a “Breaking news” [in-between the Late news sessions at JSM and the crash poster talks at machine-learning conferences] session to highlight major advances among poster submissions. And that there will be an opening talk by Steve [the Bayesian] Scott on the 4th, about the frightening prospect of MCMC death!, followed by a round-table and a welcome reception, sponsored by the Swiss Supercomputing Centre. Hence the change in dates. Which still allows for arrivals in Zürich on the January 4th [be with you].

MCMskv, Lenzerheide, Jan. 5-7, 2016

Posted in Kids, Mountains, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , on March 31, 2015 by xi'an

moonriseFollowing the highly successful [authorised opinion!, from objective sources] MCMski IV, in Chamonix last year, the BayesComp section of ISBA has decided in favour of a two-year period, which means the great item of news that next year we will meet again for MCMski V [or MCMskv for short], this time on the snowy slopes of the Swiss town of Lenzerheide, south of Zürich. The committees are headed by the indefatigable Antonietta Mira and Mark Girolami. The plenary speakers have already been contacted and Steve Scott (Google), Steve Fienberg (CMU), David Dunson (Duke), Krys Latuszynski (Warwick), and Tony Lelièvre (Mines, Paris), have agreed to talk. Similarly, the nine invited sessions have been selected and will include Hamiltonian Monte Carlo,  Algorithms for Intractable Problems (ABC included!), Theory of (Ultra)High-Dimensional Bayesian Computation, Bayesian NonParametrics, Bayesian Econometrics,  Quasi Monte Carlo, Statistics of Deep Learning, Uncertainty Quantification in Mathematical Models, and Biostatistics. There will be afternoon tutorials, including a practical session from the Stan team, tutorials for which call is open, poster sessions, a conference dinner at which we will be entertained by the unstoppable Imposteriors. The Richard Tweedie ski race is back as well, with a pair of Blossom skis for the winner!

As in Chamonix, there will be parallel sessions and hence the scientific committee has issued a call for proposals to organise contributed sessions, tutorials and the presentation of posters on particularly timely and exciting areas of research relevant and of current interest to Bayesian Computation. All proposals should be sent to Mark Girolami directly by May the 4th (be with him!).

trans-dimensional nested sampling and a few planets

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , on March 2, 2015 by xi'an

This morning, in the train to Dauphine (train that was even more delayed than usual!), I read a recent arXival of Brendon Brewer and Courtney Donovan. Entitled Fast Bayesian inference for exoplanet discovery in radial velocity data, the paper suggests to associate Matthew Stephens’ (2000)  birth-and-death MCMC approach with nested sampling to infer about the number N of exoplanets in an exoplanetary system. The paper is somewhat sparse in its description of the suggested approach, but states that the birth-date moves involves adding a planet with parameters simulated from the prior and removing a planet at random, both being accepted under a likelihood constraint associated with nested sampling. I actually wonder if this actually is the birth-date version of Peter Green’s (1995) RJMCMC rather than the continuous time birth-and-death process version of Matthew…

“The traditional approach to inferring N also contradicts fundamental ideas in Bayesian computation. Imagine we are trying to compute the posterior distribution for a parameter a in the presence of a nuisance parameter b. This is usually solved by exploring the joint posterior for a and b, and then only looking at the generated values of a. Nobody would suggest the wasteful alternative of using a discrete grid of possible a values and doing an entire Nested Sampling run for each, to get the marginal likelihood as a function of a.”

This criticism is receivable when there is a huge number of possible values of N, even though I see no fundamental contradiction with my ideas about Bayesian computation. However, it is more debatable when there are a few possible values for N, given that the exploration of the augmented space by a RJMCMC algorithm is often very inefficient, in particular when the proposed parameters are generated from the prior. The more when nested sampling is involved and simulations are run under the likelihood constraint! In the astronomy examples given in the paper, N never exceeds 15… Furthermore, by merging all N’s together, it is unclear how the evidences associated with the various values of N can be computed. At least, those are not reported in the paper.

The paper also omits to provide the likelihood function so I do not completely understand where “label switching” occurs therein. My first impression is that this is not a mixture model. However if the observed signal (from an exoplanetary system) is the sum of N signals corresponding to N planets, this makes more sense.

likelihood-free inference via classification

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , on August 5, 2014 by xi'an

IMG_2268Last week, Michael Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander posted on arXiv the last version of the paper they had presented at MCMSki 4. As indicated by its (above) title, it suggests implementing ABC based on classification tools. Thus making it somewhat connected to our recent random forest paper.

The starting idea in the paper is that datasets generated from distributions with different parameters should be easier to classify than datasets generated from distributions with the same parameters. And that classification accuracy naturally induces a distance between datasets and between the parameters behind those datasets. We had followed some of the same track when starting using random forests, before realising that for our model choice setting, proceeding the entire ABC way once the random forest procedure had been constructed was counter-productive. Random forests are just too deadly as efficient model choice machines to try to compete with them through an ABC postprocessing. Performances are just… Not. As. Good!

A side question: I have obviously never thought about that before but why is the naïve Bayes classification rule so called?! It never sounded very Bayesian to me to (a) use the true value of the parameter and (b) average the classification performances. Interestingly, the authors (i) show identical performances of other classification methods (Fig. 2) and (ii) an exception for MA time series: when we first experimented random forests, raw data from an MA(2) model was tested to select between MA(1) and  MA(2) models, and the performances of the resulting random forest were quite poor.

Now, an opposition between our two approaches is that Michael and his coauthors also include point estimation within the range of classification-based ABC inference. As we stressed in our paper, we restrict the range to classification and model choice because we do not think those machine learning tools are stable and powerful enough to perform regression and posterior probability approximation. I also see a practical weakness in the estimation scheme proposed in this new paper. Namely that the Monte Carlo gets into the way of the consistency theorem. And possibly of the simulation method itself. Another remark is that, while the authors compare the fit produced by different classification methods, there should be a way to aggregate them towards higher efficiency. Returning once more to our random forest paper, we saw improved performances each time we included a reference method, from LDA to SVMs. It would be interesting to see a (summary) variable selection version of the proposed method. A final remark is that computing time and effort do not seem to get mentioned in the paper (unless Indian jetlag confuses me more than usual). I wonder how fast the computing effort grows with the sample size to reach parametric and quadratic convergence rates.

a pseudo-marginal perspective on the ABC algorithm

Posted in Mountains, pictures, Statistics, University life with tags , , , , , , , , on May 5, 2014 by xi'an


My friends Luke Bornn, Natesh Pillai and Dawn Woodard just arXived along with Aaron Smith a short note on the convergence properties of ABC. When compared with acceptance-rejection or regular MCMC. Unsurprisingly, ABC does worse in both cases. What is central to this note is that ABC can be (re)interpreted as a pseudo-marginal method where the data comparison step acts like an unbiased estimator of the true ABC target (not of the original ABC target, mind!). From there, it is mostly an application of Christophe Andrieu’s and Matti Vihola’s results in this setup. The authors also argue that using a single pseudo-data simulation per parameter value is the optimal strategy (as compared with using several), when considering asymptotic variance. This makes sense in terms of simulating in a larger dimensional space but what of the cost of producing those pseudo-datasets against the cost of producing a new parameter? There are a few (rare) cases where the datasets are much cheaper to produce.


Get every new post delivered to your Inbox.

Join 1,021 other followers