Following the highly successful [authorised opinion!, from objective sources] MCMski IV, in Chamonix last year, the BayesComp section of ISBA has decided in favour of a two-year period, which means the great item of news that next year we will meet again for MCMski V [or MCMskv for short], this time on the snowy slopes of the Swiss town of Lenzerheide, south of Zürich. The committees are headed by the indefatigable Antonietta Mira and Mark Girolami. The plenary speakers have already been contacted and Steve Scott (Google), Steve Fienberg (CMU), David Dunson (Duke), Krys Latuszynski (Warwick), and Tony Lelièvre (Mines, Paris), have agreed to talk. Similarly, the nine invited sessions have been selected and will include Hamiltonian Monte Carlo, Algorithms for Intractable Problems (ABC included!), Theory of (Ultra)High-Dimensional Bayesian Computation, Bayesian NonParametrics, Bayesian Econometrics, Quasi Monte Carlo, Statistics of Deep Learning, Uncertainty Quantification in Mathematical Models, and Biostatistics. There will be afternoon tutorials, including a practical session from the Stan team, tutorials for which call is open, poster sessions, a conference dinner at which we will be entertained by the unstoppable Imposteriors. The Richard Tweedie ski race is back as well, with a pair of Blossom skis for the winner!
Archive for BayesComp
On Wednesday afternoon, Richard Everitt and Dennis Prangle organised an RSS workshop in Reading on Bayesian Computation. And invited me to give a talk there, along with John Hemmings, Christophe Andrieu, Marcelo Pereyra, and themselves. Given the proximity between Oxford and Reading, this felt like a neighbourly visit, especially when I realised I could take my bike on the train! John Hemmings gave a presentation on synthetic models for climate change and their evaluation, which could have some connection with Tony O’Hagan’s recent talk in Warwick, Dennis told us about “the lazier ABC” version in connection with his “lazy ABC” paper, [from my very personal view] Marcelo expanded on the Moreau-Yoshida expansion he had presented in Bristol about six months ago, with the notion that using a Gaussian tail regularisation of a super-Gaussian target in a Langevin algorithm could produce better convergence guarantees than the competition, including Hamiltonian Monte Carlo, Luke Kelly spoke about an extension of phylogenetic trees using a notion of lateral transfer, and Richard introduced a notion of biased approximation to Metropolis-Hasting acceptance ratios, notion that I found quite attractive if not completely formalised, as there should be a Monte Carlo equivalent to the improvement brought by biased Bayes estimators over unbiased classical counterparts. (Repeating a remark by Persi Diaconis made more than 20 years ago.) Christophe Andrieu also exposed some recent developments of his on exact approximations à la Andrieu and Roberts (2009).
Since those developments are not yet finalised into an archived document, I will not delve into the details, but I found the results quite impressive and worth exploring, so I am looking forward to the incoming publication. One aspect of the talk which I can comment on is related to the exchange algorithm of Murray et al. (2006). Let me recall that this algorithm handles double intractable problems (i.e., likelihoods with intractable normalising constants like the Ising model), by introducing auxiliary variables with the same distribution as the data given the new value of the parameter and computing an augmented acceptance ratio which expectation is the targeted acceptance ratio and which conveniently removes the unknown normalising constants. This auxiliary scheme produces a random acceptance ratio and hence differs from the exact-approximation MCMC approach, which target directly the intractable likelihood. It somewhat replaces the unknown constant with the density taken at a plausible realisation, hence providing a proper scale. At least for the new value. I wonder if a comparison has been conducted between both versions, the naïve intuition being that the ratio of estimates should be more variable than the estimate of the ratio. More generally, it seemed to me [during the introductory part of Christophe’s talk] that those different methods always faced a harmonic mean danger when being phrased as expectations of ratios, since those ratios were not necessarily squared integrable. And not necessarily bounded. Hence my rather gratuitous suggestion of using other tools than the expectation, like maybe a median, thus circling back to the biased estimators of Richard. (And later cycling back, unscathed, to Reading station!)
On top of the six talks in the afternoon, there was a small poster session during the tea break, where I met Garth Holloway, working in agricultural economics, who happened to be a (unsuspected) fan of mine!, to the point of entitling his poster “Robert’s paradox”!!! The problem covered by this undeserved denomination connected to the bias in Chib’s approximation of the evidence in mixture estimation, a phenomenon that I related to the exchangeability of the component parameters in an earlier paper or set of slides. So “my” paradox is essentially label (un)switching and its consequences. For which I cannot claim any fame! Still, I am looking forward the completed version of this poster to discuss Garth’s solution, but we had a beer together after the talks, drinking to the health of our mutual friend John Deely.
In connection with the previous announcement of ABC in Montréal, a call for papers that came out today:
NIPS 2014 Workshop: ABC in Montreal
December 12, 2014
Montréal, Québec, Canada
Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a large segment of the scientific community. This is particularly true for systems and population biology, computational psychology, computational chemistry, etc. Recent work has both applied machine learning models and algorithms to general ABC inference (NN, forests, GPs) and ABC inference to machine learning (e.g. using computer graphics to solve computer vision using ABC). In general, however, there is significant room for collaboration between the two communities.
The workshop will consist of invited and contributed talks, poster spotlights, and a poster session. Rather than a panel discussion we will encourage open discussion between the speakers and the audience!
Examples of topics of interest in the workshop include (but are not limited to):
* Applications of ABC to machine learning, e.g., computer vision, inverse problems
* ABC in Systems Biology, Computational Science, etc
* ABC Reinforcement Learning
* Machine learning simulator models, e.g., NN models of simulation responses, GPs etc.
* Selection of sufficient statistics
* Online and post-hoc error
* ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)
* ABC with probabilistic programming
* Posterior evaluation of scientific problems/interaction with scientists
* Post-computational error assessment
* Impact on resulting ABC inference
* ABC for model selection
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[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.
Along other members of BayesComp who launched a brainstorming session for the next MCMSki meeting before the snow has completely melted from our skis, we discussed the following topics about the future meeting:
1. Should we keep the brandname MCMSki for the incoming meetings? The argument for changing the name is that the community is broader than MCMC, as already shown by the program of MCMCSki 4. I have no strong feeling about this name, even though I find it catchy and sexy! I would thus rather keep MCMSki because it is already a brandname. Else, we could switch to M(CM)Ski, MCMSki with friends (and foes?), Snowtistics and Compuskis, or to any other short name with or without ski in it, as long as the filiation from the previous meetings is clear in the mind of the participants.
2. Should we move the frequency to two years? While the current meeting was highly popular and attracted the record number of 223 participants, and while the period right after the Winter break is not so heavily packed with meetings, we were several at a banquet table last week to object to a planned move from three to two years. I understand the appeal of meetings with great speakers in a terrific mountainous taking place as often as possible… However what stroke me with the meeting last week is that, despite the large number of parallel sessions, I overwhelmingly heard novel stuff, compared with previous meetings. And would have heard even more, had I been gifted with ubiquity. Moving to two years could cull this feeling. And induce “meeting fatigue. Furthermore, I fear that the increase in ISBA sections and the natural increase of meeting entropy pushes the percentage of meetings one can attend down and down. Sticking to a three year period would keep MCMSki more significantly attractive in that refusing an invitation would mean postponing for three years, &tc. So I personally oppose a move to two years.
3. Should we seek further financial support? The financial support behind a conference is obviously crucial. When planning MCMski 4, I however decided against contacting companies as I have no skills in the matter, but finding ways to support conference rooms, youngster travels, ski race, poster prizes and banquet would be more-than-nice. Anto’s initiative to bring a pair of skis offered by a ski company was a great one and one feat that I hope can be duplicated in the future. (During my spare week in Chamonix, I contacted ski rentals and the skipass company for a rebate, to no avail.) Travel support from ISBA and SBSS towards the travel costs of around 20 young researchers was much appreciated but is not necessarily to be found at each occurrence… Note that, despite the lack of corporate support, MCMski 4 is going to provide a positive financial return to ISBA (and BayesComp) and I strongly suggest we keep a tradition of minimalist services for the future meetings in order to fight outrageous conference fees. I think the fees should cover the conference rooms and possibly a cuppa or two a day but nothing more. In particular, the banquet should remain optional. And so should any other paying social event. (We can also do without goodies and conference material.)
4. Where should the next meeting take place? The call is on for potential organisers in either 2016 or 2017, early January. Between the Alps and the Rockies, there are plenty of possible locations, but more exotic places in the Northern Hemisphere could be suggested as well, from Lapland to Hokkaido… A question raised by Christophe Andrieu that I’d like to second is whether the preference should go to places that qualify as villages or as resort. Bormio and Chamonix are villages, while Park City is not. (I definitely prefer villages!)
Now that the conference and the Bayesian non-parametric satellite workshop (thanks to Judith!) are over, with (almost) everyone back home, and that the post-partum conference blues settles in (!), I can reflect on how things ran for those meetings and what I could have done to improve them… (Not yet considering to propose a second edition of MCMSki in Chamonix, obviously!)
Although this was clearly a side issue for most participants, the fact that the ski race did not take place still rattles me! In retrospect, adding a mere 5€ amount to the registration fees for all participants would have been enough to cover the (fairly high) fares asked by the local ski school. Late planning for the ski race led to overlook this basic fact…
Since MCMSki is now the official conference of the BayesComp section of ISBA, I should have planned well in advance a section meeting within the program, if only to discuss the structure of the next meeting and how to keep the section alive. Waiting till the end of the last section of the final day was not the best idea!
Another thing I postponed for too long was seeking some sponsors: fortunately, the O’Bayes meeting in Duke woke me up to the potential of a poster prize and re-fortunately Academic Press, CRC Press, and Springer-Verlag reacted quickly enough to have plenty of books to hand to the winners. If we could have had another group of sponsors financing a beanie or something similar, it would have been an additional perk… Even though I gathered enough support from participants about the minimalist conference “package” made of a single A4 sheet.
Last, I did not advertise properly on the webpage and at all during the meeting for the special issue of Statistics and Computing open to all presenters at MCMSki IV! We now need to send a reminder to them…
Already on the final day..! And still this frustration in being unable to attend three sessions at once… Andrew Gelman started the day with a non-computational talk that broached on themes that are familiar to readers of his blog, on the misuse of significance tests and on recommendations for better practice. I then picked the Scaling and optimisation of MCMC algorithms session organised by Gareth Roberts, with optimal scaling talks by Tony Lelièvre, Alex Théry and Chris Sherlock, while Jochen Voss spoke about the convergence rate of ABC, a paper I already discussed on the blog. A fairly exciting session showing that MCMC’ory (name of a workshop I ran in Paris in the late 90’s!) is still well and alive!
After the break (sadly without the ski race!), the software round-table session was something I was looking for. The four softwares covered by this round-table were BUGS, JAGS, STAN, and BiiPS, each presented according to the same pattern. I would have like to see a “battle of the bands”, illustrating pros & cons for each language on a couple of models & datasets. STAN got the officious prize for cool tee-shirts (we should have asked the STAN team for poster prize tee-shirts). And I had to skip the final session for a flu-related doctor appointment…
I called for a BayesComp meeting at 7:30, hoping for current and future members to show up and discuss the format of the future MCMski meetings, maybe even proposing new locations on other “sides of the Italian Alps”! But (workshop fatigue syndrome?!), no-one showed up. So anyone interested in discussing this issue is welcome to contact me or David van Dyk, the new BayesComp program chair.