Archive for seminar

last Big MC [seminar] before summer [June 19, 3pm]

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , on June 17, 2014 by xi'an

crossing Rue Soufflot on my way to IHP from Vieux Campeur, March 28, 2013Last session of our Big’MC seminar at Institut Henri Poincaré this year, on Tuesday Thursday, June 19, with

Chris Holmes (Oxford) at 3pm on

Robust statistical decisions via re-weighted Monte Carlo samples

and Pierre Pudlo (iC3M, Université de Montpellier 2) at 4:15pm on [our joint work]

ABC and machine learning

big’MC’minar next week

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , on May 9, 2014 by xi'an

crossing Rue Soufflot on my way to IHP from Vieux Campeur, March 28, 2013The next big’MC seminar in Paris will be delivered on Thursday, May 15, by

15 h : Luke Bornn, Towards the Derandomization of Markov chain Monte Carlo

16 h 15 : Pierre Jacob, On exact inference and unbiased estimation 

see the seminar webpage for more details. And make sure to attend if in or near Paris! It is definitely big and MC. Most sadly (for us!), Chris Holmes will give a Smile (Statistical machine learning) seminar at the very same time a few streets away…  At least, we can conveniently meet right after for a drink!

talk in Orsay (message in a beetle)

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

IMG_0161Yesterday (March 27), I gave a seminar at Paris-Sud University, Orsay, in the stats department, on ABC model choice. It was an opportunity to talk about recent advances we have made with Jean-Michel Marin and Pierre Pudlo on using machine-learning devices to improve ABC. (More to come soon!) And to chat with Gilles Celeux about machine learning and classification. Actually, given that one of my examples was about the Asian lady beetle invasion and that the buildings of the Paris-Sud University have suffered from this invasion, I should have advertised the talk with the more catchy title of “message in a beetle”…

This seminar was also an opportunity to experiment with mixed transportation. Indeed, since I had some errands to run in Paris in morning I decided to bike there (in Paris), work at CREST, and then take my bike in the RER train down to Orsay as I did not have the time and leisure to bike all the 20k there. Since it was the middle of the day, the carriage was mostly empty and I managed to type a blog entry without having to worry about the bike being a nuisance…. The only drag was to enter the platform in Paris (Cité Universitaire) as there was no clear access for bike. Fortunately, a student kindly helped me to get over the gate with my bike, as I could not manage on my own… Nonetheless, I will certainly repeat the experience on my next trip to Orsay (but would not dare take the bike inside/under Paris per se because of the (over-)crowded carriages there).

séminaire à Laval, Québec

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , on February 24, 2014 by xi'an

On Friday, I am giving a talk on ABC at Université Laval, in the old city of Québec. While on my way to the 14w5125 workshop on scalable Bayesian computation at BIRS, Banff. I have not visited Laval since the late 1980’s (!) even though my last trip to Québec (the city) was in 2009, when François Perron took me there for ice-climbing and skiing after a seminar in Montréal… (This trip, I will not stay long enough in Québec, alas. Keeping my free day-off for another attempt at ice-climbing near Banff.) Here are slides I have used often in the past year, but this may be the last occurrence as we are completing a paper on the topic with my friends from Montpellier.

[not] reading classics (#7)

Posted in Books, Kids, Statistics, University life with tags , , , , on December 15, 2013 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012This week, I decided not to report on the paper read at the Reading Classics student seminar, as it did not work out well-enough. The paper was the “Regression models and life-table” published in 1972 by David Cox… A classic if any! Indeed, I do not think posting a severe criticism of the presentation or the presentation itself would be of much use to anyone. It is rather sad as (a) the student clearly put some effort in the presentation, including a reproduction of an R execution, and (b) this was an entry on semi-parametrics, Kaplan-Meyer, truncated longitudinal data, and more, that could have benefited the class immensely. Alas, the talk did not take any distance from the paper, did not exploit the following discussion, and exceeded by far the allocated time, without delivering a comprehensible message. It is a complex paper with concise explanations, granted, but there were ways to find easier introductions to its contents in the more recent literature… It is possible that a second student takes over and presents her analysis of the paper next January. Unless she got so scared with this presentation that she will switch to another paper… [Season wishes to Classics Readers!]

reading classics (#4,5,6)

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , , , , on December 9, 2013 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012This week, thanks to a lack of clear instructions (from me) to my students in the Reading Classics student seminar, four students showed up with a presentation! Since I had planned for two teaching blocks, three of them managed to fit within the three hours, while the last one nicely accepted to wait till next week to present a paper by David Cox…

The first paper discussed therein was A new look at the statistical model identification, written in 1974 by Hirotugu Akaike. And presenting the AIC criterion. My student Rozan asked to give the presentation in French as he struggled with English, but it was still a challenge for him and he ended up being too close to the paper to provide a proper perspective on why AIC is written the way it is and why it is (potentially) relevant for model selection. And why it is not such a definitive answer to the model selection problem. This is not the simplest paper in the list, to be sure, but some intuition could have been built from the linear model, rather than producing the case of an ARMA(p,q) model without much explanation. (I actually wonder why the penalty for this model is (p+q)/T, rather than (p+q+1)/T for the additional variance parameter.) Or simulation ran on the performances of AIC versus other xIC’s…

The second paper was another classic, the original GLM paper by John Nelder and his coauthor Wedderburn, published in 1972 in Series B. A slightly easier paper, in that the notion of a generalised linear model is presented therein, with mathematical properties linking the (conditional) mean of the observation with the parameters and several examples that could be discussed. Plus having the book as a backup. My student Ysé did a reasonable job in presenting the concepts, but she would have benefited from this extra-week in including properly the computations she ran in R around the glm() function… (The definition of the deviance was somehow deficient, although this led to a small discussion during the class as to how the analysis of deviance was extending the then flourishing analysis of variance.) In the generic definition of the generalised linear models, I was also reminded of the
generality of the nuisance parameter modelling, which made the part of interest appear as an exponential shift on the original (nuisance) density.

The third paper, presented by Bong, was yet another classic, namely the FDR paper, Controlling the false discovery rate, of Benjamini and Hochberg in Series B (which was recently promoted to the should-have-been-a-Read-Paper category by the RSS Research Committee and discussed at the Annual RSS Conference in Edinburgh four years ago, as well as published in Series B). This 2010 discussion would actually have been a good start to discuss the paper in class, but Bong was not aware of it and mentioned earlier papers extending the 1995 classic. She gave a decent presentation of the problem and of the solution of Benjamini and Hochberg but I wonder how much of the novelty of the concept the class grasped. (I presume everyone was getting tired by then as I was the only one asking questions.) The slides somewhat made it look too much like a simulation experiment… (Unsurprisingly, the presentation did not include any Bayesian perspective on the approach, even though they are quite natural and emerged very quickly once the paper was published. I remember for instance the Valencia 7 meeting in Teneriffe where Larry Wasserman discussed about the Bayesian-frequentist agreement in multiple testing.)

reading classics (#3)

Posted in Books, Kids, Statistics, University life with tags , , , , , , on November 29, 2013 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012This week at the Reading Classics student seminar, Thomas Ounas presented a paper, Statistical inference on massive datasets, written by Li, Lin, and Li, a paper out of The List. (This paper was recently published as Applied Stochastic Models in Business and Industry, 29, 399-409..) I accepted this unorthodox proposal as (a) it was unusual, i.e., this was the very first time a student made this request, and (b) the topic of large datasets and their statistical processing definitely was interesting even though the authors of the paper were unknown to me. The presentation by Thomas was very power-pointish (or power[-point]ful!), with plenty of dazzling transition effects… Even including (a) a Python software replicating the method and (b) a nice little video on internet data transfer protocols. And on a Linux machine! Hence the experiment was worth the try! Even though the paper is a rather unlikely candidate for the list of classics… (And the rendering in static power point no so impressive. Hence a video version available as well…)

The solution adopted by the authors of the paper is one of breaking a massive dataset into blocks so that each fits into the computer(s) memory and of computing a separate estimate for each block. Those estimates are then averaged (and standard-deviationed) without a clear assessment of the impact of this multi-tiered handling of the data. Thomas then built a software to illustrate this approach, with mean and variance and quantiles and densities as quantities of interest. Definitely original! The proposal itself sounds rather basic from a statistical viewpoint: for instance, evaluating the loss in information due to using this blocking procedure requires repeated sampling, which is unrealistic. Or using solely the inter-variance estimates which seems to be missing the intra-variability. Hence to be overly optimistic. Further, strictly speaking, the method does not asymptotically apply to biased estimators, hence neither to Bayes estimators (nor to density estimators). Convergence results are thus somehow formal, in that the asymptotics cannot apply to a finite memory computer. In practice, the difficulty of the splitting technique is rather in breaking the data into blocks since Big Data is rarely made of iid observations. Think of amazon data, for instance. A question actually asked by the class. The method of Li et al. should also include some boostrapping connection. E.g., to Michael’s bag of little bootstraps.

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