Bayes on the Beach is a yearly conference taking place in Queensland Gold Coast and organised by Kerrie Mengersen and her BRAG research group at QUT. To quote from the email I just received, the conference will be held at the Mantra Legends Hotel on Surfers Paradise, Gold Coast during November 7 – 9, 2016. The conference provides a forum for discussion on developments and applications of Bayesian statistics, and includes keynote presentations, tutorials, practical problem-based workshops, invited oral presentations, and poster presentations. Abstract submissions are now open until September 2.
Archive for the Travel Category
With David Frazier and Gael Martin from Monash University, and with Judith Rousseau (Paris-Dauphine), we have now completed and arXived a paper entitled Asymptotic Properties of Approximate Bayesian Computation. This paper undertakes a fairly complete study of the large sample properties of ABC under weak regularity conditions. We produce therein sufficient conditions for posterior concentration, asymptotic normality of the ABC posterior estimate, and asymptotic normality of the ABC posterior mean. Moreover, those (theoretical) results are of significant import for practitioners of ABC as they pertain to the choice of tolerance ε used within ABC for selecting parameter draws. In particular, they [the results] contradict the conventional ABC wisdom that this tolerance should always be taken as small as the computing budget allows.
Now, this paper bears some similarities with our earlier paper on the consistency of ABC, written with David and Gael. As it happens, the paper was rejected after submission and I then discussed it in an internal seminar in Paris-Dauphine, with Judith taking part in the discussion and quickly suggesting some alternative approach that is now central to the current paper. The previous version analysed Bayesian consistency of ABC under specific uniformity conditions on the summary statistics used within ABC. But conditions for consistency are now much weaker conditions than earlier, thanks to Judith’s input!
- Li and Fearnhead (2015) considers an ABC algorithm based on kernel smoothing, whereas our interest is the original ABC accept-reject and its many derivatives
- our theoretical approach permits a complete study of the asymptotic properties of ABC, posterior concentration, asymptotic normality of ABC posteriors, and asymptotic normality of the ABC posterior mean, whereas Li and Fearnhead (2015) is only concerned with asymptotic normality of the ABC posterior mean estimator (and various related point estimators);
- the results of Li and Fearnhead (2015) are derived under very strict uniformity and continuity/differentiability conditions, which bear a strong resemblance to those conditions in Yuan and Clark (2004) and Creel et al. (2015), while the result herein do not rely on such conditions and only assume very weak regularity conditions on the summaries statistics themselves; this difference allows us to characterise the behaviour of ABC in situations not covered by the approach taken in Li and Fearnhead (2015);
Khoa Tran and Robert Kohn from UNSW just arXived a paper on a comprehensive derivation of a large range of MCMC algorithms, beyond Metropolis-Hastings. The idea is to decompose the MCMC move into
- a random completion of the current value θ into V;
- a deterministic move T from (θ,V) to (ξ,W), where only ξ matters.
If this sounds like a new version of Peter Green’s completion at the core of his 1995 RJMCMC algorithm, it is because it is indeed essentially the same notion. The resort to this completion allows for a standard form of the Metropolis-Hastings algorithm, which leads to the correct stationary distribution if T is self-inverse. This representation covers Metropolis-Hastings algorithms, Gibbs sampling, Metropolis-within-Gibbs and auxiliary variables methods, slice sampling, recursive proposals, directional sampling, Langevin and Hamiltonian Monte Carlo, NUTS sampling, pseudo-marginal Metropolis-Hastings algorithms, and pseudo-marginal Hamiltonian Monte Carlo, as discussed by the authors. Given this representation of the Markov chain through a random transform, I wonder if Peter Glynn’s trick mentioned in the previous post on retrospective Monte Carlo applies in this generic setting (as it could considerably improve convergence…)
With my daughter Rachel and her friend Clément, we tried last week to bag a few summits in the Monte Rosa massif, which stands between Italy (Aosta) and Switzerland (Zermatt). I wanted to take advantage of the Bastille Day break and we drove from Paris to Aosta in the very early morning, stopping in Chamonix to rent shoes and crampons, and meeting with our guide Abele Blanc at noon, before going together to the hut Rifugio Città di Mantova. At 3500m. Our goal was to spent the night there and climb to Punta Gnifetti (Rifugio Margherita) and Zumstein the next morning. Before heading back to Paris in the evening. However, it did not work out that way as I got a slight bout of mountain sickness that left me migrainous, nauseous, and having a pretty bad night, despite great conditions at the hut… So (despite my intense training of the previous weeks!) I did not feel that great when we left the hut at 5am. The weather was fine if cold and windy, but after two hours of moderate climbing in a fairly pleasant crispy snow of a glacier, Rachel was too out of breath to continue and Abele realised my nose had [truly] frozen (I could not feel anything!) and took us down before continuing with Clément to both peaks. This was quite a disappointment as we had planned this trip over several months, but it was clearly for the best as my fingers were definitely close to frozen (with my worst case ever of screamin’ barfies on the way down!). And we thus spent the rest of the morning waiting for our friends, warming up with tea in the sunshine. Upon reflection, planning one extra day of acclimatisation to altitude and cold would have been more reasonable and keeping handwarmers in our backpacks as well… In any case, Clément made it to the top with Abele and we got a good altitude training for the incoming San Francisco half-marathon. Plus an epic hike the next day around Cogne.
“EUA condemns strongly and unconditionally this action against universities and university staff, and expresses its heartfelt support for the higher education community in Turkey at this time.”
Following the failed attempt at a military coup in Turkey last week, Erdoğan’s government has sacked a huge number of public workers, including all Deans of Turkey’s universities and 15,200 education staff so far. Plus barring all academics from travelling abroad. Although Erdoğan’s government has been democratically elected and while the Turkish people’s actions against the military coup led it to fail, the current purge of the public sector does not proceed from democratic principles and the current Turkish constitution and laws. Further, it sounds like the crackdown is aimed at all forms of opposition rather than at those responsible for the coup, as illustrated by the closure of websites like WikiLeaks, journals and other media.