Archive for Gainesville

Challis Lectures

Posted in Books, pictures, Statistics, Travel, University life, Wines with tags , , , , , , , on November 23, 2014 by xi'an

 toatlantatoatlanta2

I had a great time during this short visit in the Department of Statistics, University of Florida, Gainesville. First, it was a major honour to be the 2014 recipient of the George H. Challis Award and I considerably enjoyed delivering my lectures on mixtures and on ABC with random forests, And chatting with members of the audience about the contents afterwards. Here is the physical award I brought back to my office:

Challis

More as a piece of trivia, here is the amount of information about the George H. Challis Award I found on the UF website:

This fund was established in 2000 by Jack M. and Linda Challis Gill and the Gill Foundation of Texas, in memory of Linda’s father, to support faculty and student conference travel awards and the George Challis Biostatistics Lecture Series. George H. Challis was born on December 8, 1911 and was raised in Italy and Indiana. He was the first cousin of Indiana composer Cole Porter. George earned a degree in 1933 from the School of Business at Indiana University in Bloomington. George passed away on May 6, 2000. His wife, Madeline, passed away on December 14, 2009.

Cole Porter, indeed!

On top of this lecturing activity, I had a full academic agenda, discussing with most faculty members and PhD students of the Department, on our respective research themes over the two days I was there and it felt like there was not enough time! And then, during the few remaining hours where I did not try to stay on French time (!), I had a great time with my friends Jim and Maria in Gainesville, tasting a fantastic local IPA beer from Cigar City Brewery and several fantastic (non-local) wines… Adding to that a pile of new books, a smooth trip both ways, and a chance encounter with Alicia in Atlanta airport, it was a brilliant extended weekend!

a pile of new books

Posted in Books, Travel, University life with tags , , , , , , , , , on November 22, 2014 by xi'an

IMG_2663I took the opportunity of my weekend trip to Gainesville to order a pile of books on amazon, thanks to my amazon associate account (and hence thanks to all Og’s readers doubling as amazon customers!). The picture above is missing two  Rivers of London volumes by Ben Aaraonovitch that I already read and left at the office. And reviewed in incoming posts. Among those,

(Obviously, all “locals” sharing my taste in books are welcome to borrow those in a very near future!)

snapshot from UF campus (#2)

Posted in pictures, Running, Travel, University life with tags , , , , on November 16, 2014 by xi'an

tree with moss

snapshot from UF campus

Posted in pictures, Running, Travel, University life with tags , , , , on November 15, 2014 by xi'an

campus

back in Gainesville (FL)

Posted in pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , on November 12, 2014 by xi'an

 

Today, I am flying to Gainesville, Florida, for the rest of the week, to give a couple of lectures. More precisely, I have actually been nominated the 2014 Challis lecturer by the Department of Statistics there, following an impressive series of top statisticians (most of them close friends, is there a correlation there?!). I am quite excited to meet again with old friends and to be back at George’s University, if only for a little less than three days. (There is a certain trend in those Fall trips as I have been going for a few days and two talks to the USA or Canada for the past three Falls: to Ames and Chicago in 2012, to Pittsburgh (CMU) and Toronto in 2013…)

Sean Meyn in Paris

Posted in Books, Statistics, Travel with tags , , , , , , , on November 23, 2013 by xi'an

My friend Sean Meyn (from the University of Florida, Gainesville) will give a talk in Paris next week (and I will be away in Coventry at the time…). Here are the details:

Mardi 26 novembre 2013 à 14h00
Salle de Conseil, 4ème étage (LINCS) 23 AVENUE D’ITALIE 75013 PARIS

Titre de l’exposé : Feature Selection for Neuro-Dynamic Programming

Neuro-Dynamic Programming encompasses techniques from both reinforcement learning and approximate dynamic programming. Feature selection refers to the choice of basis that defines the function class that is required in the application of these techniques. This talk reviews two popular approaches to neuro-dynamic programming, TD-learning and Q-learning. The main goal of this work is to demonstrate how insight from idealized models can be used as a guide for feature selection for these algorithms. Several approaches are surveyed, including fluid and diffusion models, and the application of idealized models arising from mean-field game approximations. The theory is illustrated with several examples.

intrinsic quantity for a Markov chain?

Posted in Statistics with tags , , , , , , , on February 6, 2013 by xi'an

tree next to INSEE building, Malakoff, Jan. 31, 2012I was attending a lecture this morning at CREST by Patrice Bertail where he was using estimated renewal parameters on a Markov chain to build (asymptotically) convergent bootstrap procedures. Estimating renewal parameters is obviously of interest in MCMC algorithms as they can be used to assess the convergence of the associated Markov chain: That is, if the estimation does not induce a significant bias. Another question that came to me during the talk is that; since those convergence assessments techniques are formally holding for any small set, choosing the small set in order to maximise the renewal rate also maximises the number of renewal events and hence the number of terms in the control sequence: Thus, the maximal renewal rate þ is definitely a quantity of interest: Now, is this quantity þ an intrinsic parameter of the chain, i.e. a quantity that drives its mixing and/or converging behaviour(s)? For instance; an iid sequence has a renewal rate of 1; because the whole set is a “small” set. Informally, the time between two consecutive renewal events is akin to the time between two simulations from the target and stationary distribution, according to the Kac’s representation we used in our AAP paper with Jim Hobert. So it could be that þ is directly related with the effective sample size of the chain, hence the autocorrelation. (A quick web search did not produce anything relevant:) Too bad this question did not pop up last week when I had the opportunity to discuss it with Sean Meyn in Gainesville!

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