Archive for Varanasi

Jubilee at the University of Calcutta

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , on January 2, 2017 by xi'an

The main reason for my trip to India was taking part in the celebrations of the 75th anniversary of the Department of Statistics at the University of Calcutta and of the 100th anniversary of the birth of P.K. Bose (whom I did not know before visiting Kolkata). The Department of Statistics was created in 1941 by Mahalanobis, the very first statistics department in Asia. (Mahalanobis was also instrumental in creating the ISI in 1932. And Sankhyā in 1933.)  Fisher visited Calcutta very often and was very supportive of Mahalanobis’ efforts: in the corridor, the above picture of Fisher is displayed, with him surrounded by faculties and graduates from the Department when he came in 1941.

Although I missed the first two days of the conference (!), I enjoyed very much the exchanges I had with graduate students there, about my talk on folded MCMC and other MCMC and Bayesian issues. (With The Bayesian Choice being an easy conversational bridge-way between us as it is their Bayesian textbook.) The setting reminded me of the ISBA conference in Varanasi four years ago, with the graduate students being strongly involved and providing heavy support in the organisation, as well as eager to discuss academic and non-academic issue. (Plus offering us one evening an amazing cultural show of songs and dances.) Continue reading

Varanasi unholy air

Posted in pictures, Travel with tags , , , , , , , , , , on December 15, 2016 by xi'an

candles on the Ganges in the sunset, Varanasi, Jan. 9, 2013One of my lasting memories of my trip to Varanasi four years ago is the poor quality of the air, with an almost constant fog over the city, fed by open air fires everywhere and aggressive vehicle exhaust, rather than by the few cremation pyres by the Ganges… I read today in The Guardian that the city actually ranks worst in India for its air quality. (I also read in that article that Gwalior had similar issues, although I remember a pleasant walk around the fort there, in the sun. Presumably on one of the few “good” air quality days.) Not that Paris is doing great in the past days, with a whole week of ineffective driving restrictions that left wood heating operating at full blast. I did not feel the air difference while biking, but I presume the impact of the micro-particles central to those pollution alerts is more long-term!

Current trends in Bayesian methodology with applications

Posted in Books, Statistics, Travel, University life with tags , , , , , on June 20, 2015 by xi'an

When putting this volume together with Umesh Singh, Dipak Dey, and Appaia Loganathan, my friend Satyanshu Upadhyay from Varanasi, India, asked me for a foreword. The book is now out, with chapters written by a wide variety of Bayesians. And here is my foreword, for what it’s worth:

It is a great pleasure to see a new book published on current aspects of Bayesian Analysis and coming out of India. This wide scope volume reflects very accurately on the present role of Bayesian Analysis in scientific inference, be it by statisticians, computer scientists or data analysts. Indeed, we have witnessed in the past decade a massive adoption of Bayesian techniques by users in need of statistical analyses, partly because it became easier to implement such techniques, partly because both the inclusion of prior beliefs and the production of a posterior distribution that provides a single filter for all inferential questions is a natural and intuitive way to process the latter. As reflected so nicely by the subtitle of Sharon McGrayne’s The Theory that Would not Die, the Bayesian approach to inference “cracked the Enigma code, hunted down Russian submarines” and more generally contributed to solve many real life or cognitive problems that did not seem to fit within the traditional patterns of a statistical model.
Two hundred and fifty years after Bayes published his note, the field is more diverse than ever, as reflected by the range of topics covered by this new book, from the foundations (with objective Bayes developments) to the implementation by filters and simulation devices, to the new Bayesian methodology (regression and small areas, non-ignorable response and factor analysis), to a fantastic array of applications. This display reflects very very well on the vitality and appeal of Bayesian Analysis. Furthermore, I note with great pleasure that the new book is edited by distinguished Indian Bayesians, India having always been a provider of fine and dedicated Bayesians. I thus warmly congratulate the editors for putting this exciting volume together and I offer my best wishes to readers about to appreciate the appeal and diversity of Bayesian Analysis.

Bangalore workshop [ಬೆಂಗಳೂರು ಕಾರ್ಯಾಗಾರ]

Posted in pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , on August 3, 2014 by xi'an

IISc2As I am now back home after a rather lengthy and somewhat eventful trip [getting too early to Bangalore airport with 3 hours to spend in the nice and very quiet lounge, followed by another 5 hour wait in the very nice but no so quiet Bombay airport lounge, no visit to the cockpit this time!, and then the usual sick passenger blocking all trains from Paris-Charles de Gaulle airport for one hour, reaching home to find my 97-year old neighbour fallen in her kitchen and calling for help!], I cannot but reflect on the difference between my two trips to India, from the chaos of Varanasi to the orderly peace of the campus of the Indian Institute of Science of Bangalore and even to some extent of the whole city of Bangalore, all proportions guarded. Even managing to get a [new] pair of [new] prescription glasses (or rather spectacles) within three days!

I thus found this trip much less stressful and much profitable, from enjoying the local food to discussing with Indian statisticians. The purpose of the IFCAM workshop was to bring both groups together for potential joint projects funded by IFCAM (at the travel level). While I found most talks were driven by specific applications, esp. in genomics, there are directions where we could indeed collaborate, from capture-recapture to astrostatistics. So it may be that I’ll be back in India in a near future!

mostly nuisance, little interest

Posted in Statistics, University life with tags , , , , , , on February 7, 2013 by xi'an

tree next to my bike parking garage at INSEE, Malakoff, Feb. 02, 2012Sorry for the misleading if catchy (?) title, I mean mostly nuisance parameters, very few parameters of interest! This morning I attended a talk by Eric Lesage from CREST-ENSAI on non-responses in surveys and their modelling through instrumental variables. The weighting formula used to compensate for the missing values was exactly the one at the core of the Robins-Wasserman paradox, discussed a few weeks ago by Jamie in Varanasi. Namely the one with the estimated probability of response at the denominator: The solution adopted in the talk was obviously different, with linear estimators used at most steps to evaluate the bias of the procedure (since researchers in survey sampling seem particularly obsessed with bias!)

On a somehow related topic, Aris Spanos arXived a short note (that I read yesterday) about the Neyman-Scott paradox. The problem is similar to the Robins-Wasserman paradox in that there is an infinity of nuisance parameters (the means of the successive pairs of observations) and that a convergent estimator of the parameter of interest, namely the variance common to all observations, is available. While there exist Bayesian solutions to this problem (see, e.g., this paper by Brunero Liseo), they require some preliminary steps to bypass the difficulty of this infinite number of parameters and, in this respect, are involving ad-hocquery to some extent, because the prior is then designed purposefully so. In other words, missing the direct solution based on the difference of the pairs is a wee frustrating, even though this statistic is not sufficient! The above paper by Brunero also my favourite example in this area: when considering a normal mean in large dimension, if the parameter of interest is the squared norm of this mean, the MLE ||x||² (and the Bayes estimator associated with Jeffreys’ prior) is (are) very poor: the bias is constant and of the order of the dimension of the mean, p. On the other hand, if one starts from ||x||² as the observation (definitely in-sufficient!), the resulting MLE (and the Bayes estimator associated with Jeffreys’ prior) has (have) much nicer properties. (I mentioned this example in my review of Chang’s book as it is paradoxical, gaining in efficiency by throwing away “information”! Of course, the part we throw away does not contain true information about the norm, but the likelihood does not factorise and hence the Bayesian answers differ…)

I showed the paper to Andrew Gelman and here are his comments:

Spanos writes, “The answer is surprisingly straightforward.” I would change that to, “The answer is unsurprisingly straightforward.” He should’ve just asked me the answer first rather than wasting his time writing a paper!

The way it works is as follows. In Bayesian inference, everything unknown is unknown, they have a joint prior and a joint posterior distribution. In frequentist inference, each unknowns quantity is either a parameter or a predictive quantity. Parameters do not have probability distributions (hence the discomfort that frequentists have with notation such as N(y|m,s); they prefer something like N(y;m,s) or f_N(y;m,s)), while predictions do have probability distributions. In frequentist statistics, you estimate parameters and you predict predictors. In this world, estimation and prediction are different. Estimates are evaluated conditional on the parameter. Predictions are evaluated conditional on model parameters but unconditional on the predictive quantities. Hence, mle can work well in many high-dimensional problems, as long as you consider many of the uncertain quantities as predictive. (But mle is still not perfect because of the problem of boundary estimates, e.g., here..

R finals

Posted in R, Statistics, University life with tags , , , , , , , , on January 31, 2013 by xi'an

From my office in Dauphine, on the hottest day of the year (so far)...On the morning I returned from Varanasi and the ISBA meeting there, I had to give my R final exam (along with three of my colleagues in Paris-Dauphine). This year, the R course was completely in English, exam included, which means I can post it here as it may attract more interest than the French examens of past years…

I just completed grading my 32 copies, all from exam A, which takes a while as I have to check (and sometimes recover) the R code, and often to correct the obvious mistakes to see if the deeper understanding of the concepts is there. This year student cohort is surprisingly homogeneous: I did not spot any of the horrors I may have mentioned in previous posts.

I must alas acknowledge a grievous typo in the version of Exam B that was used the day of the final: cutting-and-pasting from A to B, I forgot to change the parameters in Exercise 2, asking them to simulate a Gamma(0,1). It is only after half an hour that a bright student pointed out the impossibility… We had tested the exams prior to printing them but this somehow escaped the four of us!

Now, as I was entering my grades into the global spreadsheet, I noticed a perfect… lack of correlation between those and the grades at the midterm exam. I wonder what that means: I could be grading at random, the levels in November and in January could be uncorrelated, some students could have cheated in November and others in January, student’s names or file names got mixed up, …? A rather surprising outcome!

grades of some of my students at the midterm and finals R exams

brands with a tree logo…

Posted in Mountains, Travel with tags , , , , , , , , , on January 20, 2013 by xi'an

As the cold wave in Varanasi caught me by surprise, I asked the conference organisers for a place to buy a down jacket and they kindly drove me to a nice store called Woodland within the city. I purchased a cheap down-like jacket there (as demonstrated by the newspaper excerpt!) that solved my problem. And I thus discovered a brand that looked surprisingly similar to Timberland, slowly coming to realise this was the whole point: change Timber into Wood, slightly modify the tree in the logo, and you get a local brand that recycles Timberland designs and products to their own profit… (This seems to be a common occurrence in India, judging from this New York Times article.) Anyway, it is rather entertaining to visit the Woodland website, as they mimic major outdoor brand websites like Patagonia or Petzl, but do not offer any material one could seriously consider taking hiking and even less climbing! (Besides the jacket that managed to keep me warm for the rest of the meeting!, I also bought a cheap pair of sneakers and that quickly  proved to be a mistake, as the fit is only approximate and the material of poor quality.)