Archive for Utah

MCKSki 5, where willst thou be?!

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , on February 10, 2014 by xi'an

ridge7[Here is a call from the BayesComp Board for proposals for MCMSki 5, renamed as below to fit the BayesComp section. The earlier poll on the 'Og helped shape the proposal, with the year, 2016 vs. 2017, remaining open. I just added town to resort below as it did not sound from the poll people were terribly interested in resorts.]

The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship conference:

Bayesian Computing at MCMSki

The expectation is that the meeting will be held in January 2016, but the committee will consider proposals for other times through January 2017.

This meeting will be the next incarnation of the popular MCMSki series that addresses recent advances in the theory and application of Bayesian computational methods such as MCMC, all in the context of a world-class ski resort/town. While past meetings have taken place in the Alps and the Rocky Mountains, we encourage applications from any venue that could support MCMSki. A three-day meeting is planned, perhaps with an additional day or two of satellite meetings and/or short courses.

One page proposals should address feasibility of hosting the meeting including

1. Proposed dates.
2. Transportation for international participants (both the proximity of international airports and transportation to/from the venue).
3. The conference facilities.
4. The availability and cost of hotels, including low cost options.
5. The proposed local organizing committee and their collective experience organizing international meetings.
6. Expected or promised contributions from the host organization, host country, or industrial partners towards the cost of running the meetings.

Proposals should be submitted to David van Dyk (dvandyk, BayesComp Program Chair) at imperial.ac.uk no later than May 31, 2014.

The Board of Bayesian Computing Section will evaluate the proposals, choose a venue, and appoint the Program Committee for Bayesian Computing at MCMSki.

MCMski IV (homepage)

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on October 23, 2012 by xi'an

I have rewritten the text on the home page of the MCMSki IV website. Feel free to comment! I also want to signal the creation of a Facebook page.

The next MCMSki meeting, MCMSki IV, will be held in Chamonix Mont-Blanc, France, from Monday, January 6 to Wednesday, January 8, 2014. As for the previous MCMSki meetings, it jointly supported by the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), as the first meeting of the newly created BayesComp section of ISBA. It will focus on all aspects of MCMC theory and methodology, including related fields like sequential Monte Carlo, approximate Bayesian computation, Hamiltonian Monte Carlo. In contrast with the earlier meetings, it will merge the satellite Adap’ski workshop into the main meeting by having parallel (invited and contributed) sessions on those different themes. A call for proposals of sessions and talks is available here. There will be also opportunities for presenting one’s work at plenary and well-attended evening poster sessions.

In terms of location, after an excursion to Utah, MCMSki IV is back in the Alps, on the French side of Mont-Blanc, and Chamonix offers a wide range of outdoor and indoor activities during the breaks, with all levels of skiing available. The meeting will take place at the Conference Centre le Majestic (Centre des Congrès – Le Majestic) in Chamonix Mont-Blanc. (With a large population of English expatriates living there, Chamonix is very easy to handle for English speakers. The lodging capacities are both diverse and plenty.)

On optimality of kernels for ABC-SMC

Posted in Statistics, University life with tags , , , , , , , , , , on December 11, 2011 by xi'an

This freshly arXived paper by Sarah Filippi, Chris Barnes, Julien Cornebise, and Michael Stumpf, is in the lineage of our 2009 Biometrika ABC-PMC (population Monte Carlo) paper with Marc Beaumont, Jean-Marie Cornuet and Jean-Michel Marin. (I actually missed the first posting while in Berlin last summer. Flying to Utah gave me the opportunity to read it at length!)  The  paper focusses on the impact of the transition kernel in our PMC scheme: while we used component-wise adaptive proposals, the paper studies multivariate adaptivity with a covariance matrix adapted from the whole population, or locally or from an approximation to the information matrix. The simulation study run in the paper shows that, even when accounting for the additional cost due to the derivation of the matrix, the multivariate adaptation can improve the acceptance rate by a fair amount. So this is an interesting and positive sequel to our paper (that I may well end up refereeing one of those days, like an earlier paper from some of the authors!)

The main criticism I may have about the paper is that the selection of the tolerance sequence is not done in an adaptive way, while it could, given the recent developments of Del Moral et al. and of Drovandri and Pettitt (as well as our even more recent still-un-arXived submission to Stat & Computing!). While the target is the same for all transition kernels, thus the comparison still makes sense as is, the final product is to build a complete adaptive scheme that comes as close as possible to the genuine posterior.

This paper also raised a new question: there is a slight distinction between the Kullback-Leibler divergence we used and the Kullback-Leibler divergence the authors use here. (In fact, we do not account for the change in the tolerance.) Now, since what only matters is the distribution of the current particles, and while the distribution on the past particles is needed to compute the double integral leading to the divergence, there is a complete freedom in the choice of this past distribution. As in Del Moral et al., the distribution L(θ:t-1t) could therefore be chosen towards an optimal acceptance rate or something akin. I wonder if anyone ever looked at this…


Bridal Veil fall, Provo

Posted in Mountains, pictures, Travel with tags , , , on December 10, 2011 by xi'an

On Friday, Shane Reese (who so superbly organised the MCMSki III conference early this year and helped us so much for the Adap’ skiii workshop!) took me ice-climbing on one of the most iconic ice routes near Provo, Bridal Veil Fall. There, we met with a guide, Scott Adamson, who lead-climbed both pitches we experimented and belayed us as well.

This was a superb day of climbing where we did about six pitches each, including an attempt towards mixed climbing which was very interesting in its closer connection with rock climbing. Scott was immensely encouraging and it was only towards the end of the day that he told us about the new route he had opened on Moose’s Tooth, Denali, a story I had read in a climbing magazine at that time… (He was trying another route there last spring as well.)

Provo, Utah

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , on December 9, 2011 by xi'an

Prior to my attending WSC 2011 in Phoenix next Sunday, I was invited to give a seminar at Brigham Young University in Provo, by the department of Statistics. (This is a private religious university run by the LDS Church, named after of the founders. Students and faculty have to adhere to an “Honor Code” that prohibits, among other things, tea. As an illicit substance. Fortunately, this does not apply to visitors and I can keep drinking tea all night.) The surroundings of Provo are superb,  especially in the current crisp dry weather, the forefront of the Wasatch mountains being the actual Eastern boundary of the town. I hope to get some ice-climbing done today, as Provo is a great spot for doing this!

The visit to the department was very pleasant with a very warm welcome by all and a lot of interesting discussions. I gave my seminar on ABC model choice, using the slides already presented in Madrid last month:

which is quite appropriate given that one of the papers (about the limitations of ABC model choice) was conceived in Utah, early this year (at the MCMC’Ski conference). There were an amazing lot of graduate students in the audience and I hope I managed to get the message out to them, despite the heavy math part at the end. (I personally got a better understand of [A4] and of a way to rewrite it slightly more clearly. I also spotted a typo on mad(y) that should have been corrected weeks ago once Natesh had mentioned it!) Natalie Blades gave a very kind (if rather embarrassing!) intro to my talk and concluded with a “two truths and a lie” game with the audience, asking which one of three facts

  • I worked in a Camembert cheese factory
  • I played the trumpet in a French Navy band
  • I climbed Mont Blanc

was a lie. Producing a very interesting outcome!

yet more questions about Monte Carlo Statistical Methods

Posted in Books, Statistics, University life with tags , , , , , , , , , , on December 8, 2011 by xi'an

As a coincidence, here is the third email I this week about typos in Monte Carlo Statistical Method, from Peng Yu this time. (Which suits me well in terms of posts as  I am currently travelling to Provo, Utah!)

I’m reading the section on importance sampling. But there are a few cases in your book MCSM2 that are not clear to me.

On page 96: “Theorem 3.12 suggests looking for distributions g for which |h|f/g is almost constant with finite variance.”

What is the precise meaning of “almost constant”? If |h|f/g is almost constant, how come its variance is not finite?

“Almost constant” is not a well-defined property, I am afraid. By this sentence on page 96 we meant using densities g that made |h|f/g as little varying as possible while being manageable. Hence the insistence on the finite variance. Of course, the closer |h|f/g is to a constant function the more likely the variance is to be finite.

“It is important to note that although the finite variance constraint is not necessary for the convergence of (3.8) and of (3.11), importance sampling performs quite poorly when (3.12) ….”

It is not obvious to me why when (3.12) importance sampling performs poorly. I might have overlooked some very simple facts. Would you please remind me why it is the case? From the previous discussion in the same section, it seems that h(x) is missing in (3.12). I think that (3.12) should be (please compare with the first equation in section 3.3.2)

\int h^2(x) f^2(x) / g(x) \text{d}x = + \infty

The preference for a finite variance of f/g and against (3.12) is that we would like the importance function g to work well for most integrable functions h. Hence a requirement that the importance weight f/g itself behaves well. It guarantees some robustness across the h‘s and also avoids checking for the finite variance (as in your displayed equation) for all functions h that are square-integrable against g, by virtue of the Cauchy-Schwarz inequality.

published in PNAS!

Posted in Statistics, University life with tags , , , , on August 30, 2011 by xi'an

The paper “Lack of confidence in approximate Bayesian computation model choice“, with  Jean-Marie Cornuet, Jean-Michel Marin, and Natesh S. Pillai, has now appeared in the Early Edition of PNAS! It is in Open Access, so fully accessible to everyone. Thanks to the referees and to the PNAS editor, Steve Fienberg, for their support. A very fitting ending for a paper started around a (fake) log-fire in Park City! (And my very first paper in PNAS!)

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