Advances in scalable Bayesian computation [day #4]

Final day of our workshop Advances in Scalable Bayesian Computation already, since tomorrow morning is an open research time ½ day! Another “perfect day in paradise”, with the Banff Centre campus covered by a fine snow blanket, still falling…, and making work in an office of BIRS a dream-like moment.

Still looking for a daily theme, parallelisation could be the right candidate, even though other talks this week went into parallelisation issues, incl. Steve’s talk yesterday. Indeed, Anthony Lee gave a talk this morning on interactive sequential Monte Carlo, where he motivated the setting by a formal parallel structure. Then, Darren Wilkinson surveyed the parallelisation issues in Monte Carlo, MCMC, SMC and ABC settings, before arguing in favour of a functional language called Scala. (Neat entries to those topics can be found on Darren’s blog.) And in the afternoon session, Sylvia Frühwirth-Schnatter exposed her approach to the (embarrassingly) parallel problem, in the spirit of Steve’s , David Dunson’s and Scott’s (a paper posted on the day I arrived in Chamonix and hence I missed!). There was plenty to learn from that talk (do not miss the Yin-Yang moment at 25 mn!), but it also helped me to break a difficulty I had with the consensus Bayes representation for two weeks (more on that later!). And, even though Marc Suchard mostly talked about flu and trees in a very pleasant and broad talk, he also had a slide on parallelisation to fit the theme! Although unrelated with parallelism,  Nicolas Chopin’s talk was on sequential quasi-Monte Carlo algorithms: while I had heard previous versions of this talk in Chamonix and BigMC, I found it full of exciting stuff. And it clearly got the room truly puzzled by this possibility, in a positive way! Similarly, Alex Lenkoski spoke about extreme rain events in Norway with no trace of parallelism, but the general idea behind the examples was to question the notion of the calibrated Bayesian (with possible connections with the cut models).

This has been a wonderful week and I am sure the participants got as much as I did from the talks and the informal exchanges. Thanks to BIRS for the sponsorship and the superb organisation of the week (and to the Banff Centre for providing such a paradisical environment). I feel very privileged to have benefited from this support, even though I deadly hope to be back in Banff within a few years.

MCMSki IV [day 2.5]

Despite a good rest during the ski break, my cold did not get away (no magic left in this world!) and I thus had a low attention span to attend the Bayesian statistics and Population genetics session: while Jukka Corander mentioned the improvement brought by our AMIS algorithm, I had difficulties getting the nature of the model, if only because he used a blackboard-like font that made math symbols too tiny to read. (Nice fonts, otherwise!), Daniel Lawson (of vomiting Warhammer fame!) talked about the alluring notion of a statistical emulator, and Barbara Engelhardt talked about variable selection in a SNP setting. I did not get a feeling on how handling ten millions of SNPs was possible in towards a variable selection goal.  My final session of the day was actually “my” invited session on ABC methods, where Richard Everitt presented a way of mixing exact approximation with ABC and synthetic likelihood (Wood, Nature) approximations. The resulting MAVIS algorithm is  not out yet. The second speaker was Ollie Ratman, who spoke on his accurate ABC that I have discussed many times here. And Jean-Michel Marin managed to drive from Montpelier, just in time to deliver his talk on our various explorations of the ABC model choice problem.

After a quick raclette at “home”, we headed back to the second poster session, where I had enough of a clear mind and not too much of a headache (!) to have several interesting discussions, incl. a new parallelisation suggested  by Ben Calderhead, the sticky Metropolis algorithm of Luca Martino, the airport management video of Jegar Pitchforth, the mixture of Dirichlet distributions for extremes by Anne Sabourin, not mentioning posters from Warwick or Paris. At the end of the evening  I walked back to my apartment with the Blossom skis we had brought in the morning to attract registrations for the ski race: not enough to make up for the amount charged by the ski school. Too bad, especially given Anto’s efforts to get this amazing sponsoring!

Good size swans and turkeys

In connection with The Black Swan, Nassim Taleb wrote a small essay called The Fourth Quadrant on The Edge. I found it much more pleasant to read than the book because (a) it directly focus on the difficulty of dealing with fat tail distributions and the prediction of extreme events, and (b) it is delivered in a much more serene tone than the book (imagine, just the single remark about the Frenchs!). The text contains insights on loss functions and inverse problems which, even though they are a bit vague, do mostly make sense. As for The Black Swan, I deplore (a) the underlying determinism of the author, which still seems to believe in an unknown (and possibly unachievable) model that would rule the phenomenon under study and (b) the lack of temporal perspective and of the possibility of modelling jumps as changepoints, i.e. model shifts. Time series have no reason to be stationary, the less so the more they depend on all kinds of exogeneous factors. I actually agree with Taleb that, if there is no information about the form of the tails of the distribution corresponding to the phenomenon under study—assuming there does exist a distribution—, estimating the shape of this tail from the raw data is impossible.

The essay is followed by a technical appendix that expands on fat tails, but not so deeply as to be extremely interesting. A surprising side note is that Taleb seems to associate stochastic volatility with mixtures of Gaussians. In my personal book of models, stochastic volatility is a noisy observation of the exponential of a random walk, something likethus with much higher variation (and possibly no moments). To state that Student’s t distributions are more variable than stochastic volatility models is therefore unusual… There is also an analysis over a bizillion datasets of the insanity of computing kurtosis when the underlying distribution may not have even a second moment. I could not agree more: trying to summarise fat tail distributions by their four first moments does not make sense, even though it may sell well. The last part of the appendix shows the equal lack of stability of estimates of the tail indexwhich again is not a surprising phenomenon: if the tail bound K is too low, it may be that the power law has not yet quicked in while, if it is too large, then we always end up with not enough data. The picture shows how the estimate widely varies with K around its theoretical value for the log-normal and three Pareto distributions, based on a million simulations. (And this is under the same assumption of stationarity as above.) So I am not sure what the message is there. (As an aside, there seems to be a mistake in the tail expectation: it should be

if the density decreases inIt is correct whenis the tail power of the cdf.)