Archive for the University life Category

a trip back in time [and in Rouen]

Posted in Kids, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on June 24, 2017 by xi'an

On Monday, I took part in a celebration of the remarkable career of a former colleague of mine in Rouen, Gérard Grancher, who is retiring after a life-long position as CNRS engineer in the department of maths of the University of Rouen, a job title that tells very little about the numerous facets of his interactions with mathematics, from his handling of all informatics aspects in the laboratory to his support of all colleagues there, including fresh PhD students like me in 1985!, to his direction of the CNRS lab in 2006 and 2007 at a time of deep division and mistrust, to his numerous collaborations on statistical projects with local actors, to his Norman federalism in bringing the maths departments of Caen and Rouen into a regional federation, to an unceasing activism to promote maths in colleges and high schools and science fairs all around Normandy, to his contributions to professional training in statistics for CNRS agents, and much, much more… Which explains why the science auditorium of the University of Rouen was packed with mathematicians and high schools maths teachers and friends! (The poster of the day was made by Gérard’s accomplices in vulgarisation, Élise Janvresse and Thierry Delarue, based on a sample of points randomly drawn from Gérard’s picture, maybe using a determinantal process, and the construction of a travelling salesman path over those points.)

This was a great day with mostly vulgarisation talks (including one about Rasmus’ socks..!) and reminiscences about Gérard’s carreer at Rouen. As I had left the university in 2000 to move to Paris-Dauphine, this was a moving day as well, as I met with old friends I had not seen for ages, including our common PhD advisor, Jean-Pierre Raoult.

This trip back in time was also an opportunity to (re-)visit the beautifully preserved medieval centre of Rouen, with its wooden houses, Norman-style, the numerous churches, including Monet‘s cathedral, the Justice Hall… Last time I strolled those streets, George Casella was visiting!

slice sampling for Dirichlet mixture process

Posted in Books, Statistics, University life with tags , , , , , , , on June 21, 2017 by xi'an

When working with my PhD student Changye in Dauphine this morning I realised that slice sampling also applies to discrete support distributions and could even be of use in such settings. That it works is (now) straightforward in that the missing variable representation behind the slice sampler also applies to densities defined with respect to a discrete measure. That this is useful transpires from the short paper of Stephen Walker (2007) where we saw this, as Stephen relies on the slice sampler to sample from the Dirichlet mixture model by eliminating the tail problem associated with this distribution. (This paper appeared in Communications in Statistics and it is through Pati & Dunson (2014) taking advantage of this trick that Changye found about its very existence. I may have known about it in an earlier life, but I had clearly forgotten everything!)

While the prior distribution (of the weights) of the Dirichlet mixture process is easy to generate via the stick breaking representation, the posterior distribution is trickier as the weights are multiplied by the values of the sampling distribution (likelihood) at the corresponding parameter values and they cannot be normalised. Introducing a uniform to replace all weights in the mixture with an indicator that the uniform is less than those weights corresponds to a (latent variable) completion [or a demarginalisation as we called this trick in Monte Carlo Statistical Methods]. As elaborated in the paper, the Gibbs steps corresponding to this completion are easy to implement, involving only a finite number of components. Meaning the allocation to a component of the mixture can be operated rather efficiently. Or not when considering that the weights in the Dirichlet mixture are not monotone, hence that a large number of them may need to be computed before picking the next index in the mixture when the uniform draw happens to be quite small.

The Seven Pillars of Statistical Wisdom [book review]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on June 10, 2017 by xi'an

I remember quite well attending the ASA Presidential address of Stephen Stigler at JSM 2014, Boston, on the seven pillars of statistical wisdom. In connection with T.E. Lawrence’s 1926 book. Itself in connection with Proverbs IX:1. Unfortunately wrongly translated as seven pillars rather than seven sages.

As pointed out in the Acknowledgements section, the book came prior to the address by several years. I found it immensely enjoyable, first for putting the field in a (historical and) coherent perspective through those seven pillars, second for exposing new facts and curios about the history of statistics, third because of a literary style one would wish to see more often in scholarly texts and of a most pleasant design (and the list of reasons could go on for quite a while, one being the several references to Jorge Luis Borges!). But the main reason is to highlight the unified nature of Statistics and the reasons why it does not constitute a subfield of either Mathematics or Computer Science. In these days where centrifugal forces threaten to split the field into seven or more disciplines, the message is welcome and urgent.

Here are Stephen’s pillars (some comments being already there in the post I wrote after the address):

  1. aggregation, which leads to gain information by throwing away information, aka the sufficiency principle. One (of several) remarkable story in this section is the attempt by Francis Galton, never lacking in imagination, to visualise the average man or woman by superimposing the pictures of several people of a given group. In 1870!
  2. information accumulating at the √n rate, aka precision of statistical estimates, aka CLT confidence [quoting  de Moivre at the core of this discovery]. Another nice story is Newton’s wardenship of the English Mint, with musing about [his] potential exploiting this concentration to cheat the Mint and remain undetected!
  3. likelihood as the right calibration of the amount of information brought by a dataset [including Bayes’ essay as an answer to Hume and Laplace’s tests] and by Fisher in possible the most impressive single-handed advance in our field;
  4. intercomparison [i.e. scaling procedures from variability within the data, sample variation], from Student’s [a.k.a., Gosset‘s] t-test, better understood and advertised by Fisher than by the author, and eventually leading to the bootstrap;
  5. regression [linked with Darwin’s evolution of species, albeit paradoxically, as Darwin claimed to have faith in nothing but the irrelevant Rule of Three, a challenging consequence of this theory being an unobserved increase in trait variability across generations] exposed by Darwin’s cousin Galton [with a detailed and exhilarating entry on the quincunx!] as conditional expectation, hence as a true Bayesian tool, the Bayesian approach being more specifically addressed in (on?) this pillar;
  6. design of experiments [re-enters Fisher, with his revolutionary vision of changing all factors in Latin square designs], with an fascinating insert on the 18th Century French Loterie,  which by 1811, i.e., during the Napoleonic wars, provided 4% of the national budget!;
  7. residuals which again relate to Darwin, Laplace, but also Yule’s first multiple regression (in 1899), Fisher’s introduction of parametric models, and Pearson’s χ² test. Plus Nightingale’s diagrams that never cease to impress me.

The conclusion of the book revisits the seven pillars to ascertain the nature and potential need for an eight pillar.  It is somewhat pessimistic, at least my reading of it was, as it cannot (and presumably does not want to) produce any direction about this new pillar and hence about the capacity of the field of statistics to handle in-coming challenges and competition. With some amount of exaggeration (!) I do hope the analogy of the seven pillars that raises in me the image of the beautiful ruins of a Greek temple atop a Sicilian hill, in the setting sun, with little known about its original purpose, remains a mere analogy and does not extend to predict the future of the field! By its very nature, this wonderful book is about foundations of Statistics and therefore much more set in the past and on past advances than on the present, but those foundations need to move, grow, and be nurtured if the field is not to become a field of ruins, a methodology of the past!

fast ε-free ABC

Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on June 8, 2017 by xi'an

Last Fall, George Papamakarios and Iain Murray from Edinburgh arXived an ABC paper on fast ε-free inference on simulation models with Bayesian conditional density estimation, paper that I missed. The idea there is to approximate the posterior density by maximising the likelihood associated with a parameterised family of distributions on θ, conditional on the associated x. The data being then the ABC reference table. The family chosen there is a mixture of K Gaussian components, which parameters are then estimated by a (Bayesian) neural network using x as input and θ as output. The parameter values are simulated from an adaptive proposal that aims at approximating the posterior better and better. As in population Monte Carlo, actually. Except for the neural network part, which I fail to understand why it makes a significant improvement when compared with EM solutions. The overall difficulty with this approach is that I do not see a way out of the curse of dimensionality: when the dimension of θ increases, the approximation to the posterior distribution of θ does deteriorate, even in the best of cases, as any other non-parametric resolution. It would have been of (further) interest to see a comparison with a most rudimentary approach, namely the one we proposed based on empirical likelihoods.

from least squares to signal processing and particle filtering

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , on June 6, 2017 by xi'an

Nozer Singpurwalla, Nick. Polson, and Refik Soyer have just arXived a remarkable survey on the history of signal processing, from Gauß, Yule, Kolmogorov and Wiener, to Ragazzini, Shanon, Kálmán [who, I was surprised to learn, died in Gainesville last year!], Gibbs sampling, and the particle filters of the 1990’s.

efficient acquisition rules for ABC

Posted in pictures, Statistics, University life with tags , , , , , , , , on June 5, 2017 by xi'an

A few weeks ago, Marko Järvenpää, Michael Gutmann, Aki Vehtari and Pekka Marttinen arXived a paper on sampling design for ABC that reminded me of presentations Michael gave at NIPS 2014 and in Banff last February. The main notion is that, when the simulation from the model is hugely expensive, random sampling does not make sense.

“While probabilistic modelling has been used to accelerate ABC inference, and strategies have been proposed for selecting which parameter to simulate next, little work has focused on trying to quantify the amount of uncertainty in the estimator of the ABC posterior density itself.”

The above question  is obviously interesting, if already considered in the literature for it seems to focus on the Monte Carlo error in ABC, addressed for instance in Fearnhead and Prangle (2012), Li and Fearnhead (2016) and our paper with David Frazier, Gael Martin, and Judith Rousseau. With corresponding conditions on the tolerance and the number of simulations to relegate Monte Carlo error to a secondary level. And the additional remark that the (error free) ABC distribution itself is not the ultimate quantity of interest. Or the equivalent (?) one that ABC is actually an exact Bayesian method on a completed space.

The paper initially confused me for a section on the very general formulation of ABC posterior approximation and error in this approximation. And simulation design for minimising this error. It confused me as it sounded too vague but only for a while as the remaining sections appear to be independent. The operational concept of the paper is to assume that the discrepancy between observed and simulated data, when perceived as a random function of the parameter θ, is a Gaussian process [over the parameter space]. This modelling allows for a prediction of the discrepancy at a new value of θ, which can be chosen as maximising the variance of the likelihood approximation. Or more precisely of the acceptance probability. While the authors report improved estimation of the exact posterior, I find no intuition as to why this should be the case when focussing on the discrepancy, especially because small discrepancies are associated with parameters approximately generated from the posterior.


Posted in R, Statistics, University life with tags , , , , , , , on June 4, 2017 by xi'an

A few weeks ago and then some, I [as occasional blogger!] got contacted by to write a piece on this data-sharing platform. I then went and checked what this was all about, having the vague impression this was a platform where I could store and tun R codes, besides dropping collective projects, but from what I quickly read, it sounds more like being able to run R scripts from one’s machine using data and code stored on But after reading just one more blog entry I finally understood it is also possible to run R, SQL, NotebookJS (and LaTeX) directly on that platform, without downloading code or data to one’s machine. Which makes it a definitive plus with this site, as users can experiment with no transfer to their computer. Hence on a larger variety of platforms. While personally I do not [yet?] see how to use it for my research or [limited] teaching, it seems like an [yet another] interesting exploration of the positive uses of Internet to collaborate and communicate on scientific issues! With no opinion on privacy and data protection offered by the site, of course.