Archive for Gainesville
snapshot from UF campus (#2)
Posted in pictures, Running, Travel, University life with tags Florida, Gainesville, Griffin-Floyd Hall, Spanish moss, University of Florida on November 16, 2014 by xi'an
snapshot from UF campus
Posted in pictures, Running, Travel, University life with tags Challis Lecture, Florida, Gainesville, University of Florida, USA on November 15, 2014 by xi'an
back in Gainesville (FL)
Posted in pictures, Running, Statistics, Travel, University life, Wines with tags ABC, Challis Lecture, finite mixtures, Florida, Gainesville, George Casella, random forests, seminar, University of Florida 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 dynamic programming, Florida, Gainesville, Markov chains, Paris, reinforcement learning, Sean Meyn, seminar on November 23, 2013 by xi'anMy 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 PARISTitre 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 þ, bootstrap, convergence assessment, CREST, effective sample size, Gainesville, Markov chain Monte Carlo, renewal process on February 6, 2013 by xi'an
I 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!
