Today, I made a quick TGV trip to Besançon, in French Jura, to give a seminar to astronomers and physicists, in connection with the Gaia project I had mentioned earlier. I gave my talk straight out of the train and then we started discussing MCMC and ABC for the astronomy problems my guests face. To my surprise, I discovered that they do run some local form of ABC, using their own statistics and distances to validate simulation from the (uniform) prior on their parameter space. The discussion went far enough to take a peek under the hood, namely to look at some Fortran programs they are running (and make suggestions for acceleration and adaptation). It is quite interesting to see that ABC is actually a natural approach when people face complex likelihoods and that, while they construct appropriate tools, they feel somehow uncertain about the validation of those methods and are unaware of very similar tools in other fields. In addition to this great day of exchange, I had several hours of freedom in the train (and a plug) to work on the bayess package for Bayesian Essentials (not dead yet!). Here are my slides, pot-pourri of earlier talks. (Including the one on cosmology model choice in Vancouver.)
Archive for Vancouver
The Brazilian society for Bayesian Analysis (ISBrA, whose annual meeting is taking place at this very time!) asked me to write a review on Pierre Simon Laplace’s book, Théorie Analytique des Probabilités, a book that was initially published in 1812, exactly two centuries ago. I promptly accepted this request as (a) I had never looked at this book and so this provided me with a perfect opportunity to do so, (b) while in Vancouver, Julien Cornebise had bought for me a 1967 reproduction of the 1812 edition, (c) I was curious to see how much of the book had permeated modern probability and statistics or, conversely, how much of Laplace’s perspective was still understandable by modern day standards. (Note that the link on the book leads to a free version of the 1814, not 1812, edition of the book, as free as the kindle version on amazon.)
“Je m’attache surtout, à déterminer la probabilité des causes et des résultats indiqués par événemens considérés en grand nombre.” P.S. Laplace, Théorie Analytique des Probabilités, page 3
First, I must acknowledge I found the book rather difficult to read and this for several reasons: (a) as is the case for books from older times, the ratio text-to-formulae is very high, with an inconvenient typography and page layout (ar least for actual standards), so speed-reading is impossible; (b) the themes offered in succession are often abruptly brought and uncorrelated with the previous ones; (c) the mathematical notations are 18th-century, so sums are indicated by S, exponentials by c, and so on, which again slows down reading and understanding; (d) for all of the above reasons, I often missed the big picture and got mired into technical details until they made sense or I gave up; (e) I never quite understood whether or not Laplace was interested in the analytics like generating functions only to provide precise numerical approximations or for their own sake. Hence a form of disappointment by the end of the book, most likely due to my insufficient investment in the project (on which I mostly spent an Amsterdam/Calgary flight and jet-lagged nights at BIRS…), even though I got excited by finding the bits and pieces about Bayesian estimation and testing. Continue reading
As posted in early August from JSM 2010 in Vancouver, StatProb was launched as a way to promote an on-line encyclopedia/wiki with the scientific backup of expert reviewers. This was completely novel and I was quite excited to take part in the venture as a representative of the Royal Statistical Society. Most unfortunately, the separation of the originator of the project, John Kimmel, and of the editor Springer-Verlag (which is backing up the project) a few weeks later put an almost sure stop to the experiment by exposing the lack of incentive in investing a not-inconsiderable amount of our time in editing the entries and the need for part-time operators that would handle LaTeX and other editorial issues… The core of the matter is, I think, that the “reward” in getting involved in the wiki is sadly too limited from an academic perspective to balance the investment (the more because most members of the editorial board were senior researchers). This was clear for instance in the search of a person in charge of the LaTeX aspects of the submissions: I could not find a strong enough reason to convince a younger colleague to dedicate part of his (limitless!) energy to this task, apart from service to the community… So, in the end, and in agreement with the Royal Statistical Society, I have sadly resigned from the board of StatProb along with George Casella and Nando de Freitas.
In the wake of the main machine learning NIPS 2010 meeting in Vancouver, Dec. 6-9 2010, there will be a very interesting workshop organised by Ryan Adams, Mark Girolami, and Iain Murray on Monte Carlo Methods for Bayesian Inference in Modern Day Applications, on Dec. 10. (And in Whistler, not Vancouver!) I wish I could attend, but going to a conference in honour of Larry Brown’s 70th birthday in Wharton the week after makes it impossible…
Monte Carlo methods have been the dominant form of approximate inference for Bayesian statistics over the last couple of decades. Monte Carlo methods are interesting as a technical topic of research in themselves, as well as enjoying widespread practical use. In a diverse number of application areas Monte Carlo methods have enabled Bayesian inference over classes of statistical models which previously would have been infeasible. Despite this broad and sustained attention, it is often still far from clear how best to set up a Monte Carlo method for a given problem, how to diagnose if it is working well, and how to improve under-performing methods. The impact of these issues is even more pronounced with new emerging applications.
What does the workshop address and accomplish?
Identifying features of applications of Monte Carlo methods: This workshop is aimed equally at practitioners and core Monte Carlo researchers. For practitioners we hope to identify what properties of applications are important for selecting, running and checking a Monte Carlo algorithm. Monte Carlo methods are applied to a broad variety of problems. The workshop aims to identify and explore what properties of these disparate areas are important to think about when applying Monte Carlo methods.