## lock in [book review]

Posted in Books, Kids, Travel with tags , , , , , , on January 17, 2015 by xi'an

As mentioned in my recent review of Redshirts, I was planning to read John Scalzi’s most recent novel, Lock In, if only to check whether or not Redshirts was an isolated accident! This was the third book from “the pile” that I read through the Yule break and, indeed, it was a worthwhile attempt as the book stands miles above Redshirts

The story is set in a very convincing near-future America where a significant part of the population is locked by a super-flu into a full paralysis that forces them to rely on robot-like interfaces to interact with unlocked humans. While the book is not all that specific on how the robotic control operates, except from using an inserted “artificial neural network” inside the “locked-in” brains, Scalzi manages to make it sound quite realistic, with societal and corporation issues at the forefront. To the point of selling really well the (usually lame) notion of instantaneous relocation at the other end of the US. And with the bare minimum of changes to the current society, which makes it easier to buy. I have not been that enthralled by a science-fiction universe for quite a while. I also enjoyed how the economics of this development of a new class of citizens was rendered, the book rotating around the consequences of the ending of heavy governmental intervention in lock in research.

Now, the story itself is of a more classical nature in that the danger threatening the loked-in population is uncovered single-handedly by the rookie detective who conveniently happens to be the son of a very influential ex-basketball-player and hence to meet all the characters involved in the plot. This is pleasant but somewhat thin with a limited number of players considering the issues at stake and a rather artificial ending.

Look here for a more profound review by Cory Doctorow.

## Advances in scalable Bayesian computation [day #4]

Posted in Books, Mountains, pictures, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , on March 7, 2014 by xi'an

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.

## cut, baby, cut!

Posted in Books, Kids, Mountains, R, Statistics, University life with tags , , , , , , , , , , , , , on January 29, 2014 by xi'an

At MCMSki IV, I attended (and chaired) a session where Martyn Plummer presented some developments on cut models. As I was not sure I had gotten the idea [although this happened to be one of those few sessions where the flu had not yet completely taken over!] and as I wanted to check about a potential explanation for the lack of convergence discussed by Martyn during his talk, I decided to (re)present the talk at our “MCMSki decompression” seminar at CREST. Martyn sent me his slides and also kindly pointed out to the relevant section of the BUGS book, reproduced above. (Disclaimer: do not get me wrong here, the title is a pun on the infamous “drill, baby, drill!” and not connected in any way to Martyn’s talk or work!)

I cannot say I get the idea any clearer from this short explanation in the BUGS book, although it gives a literal meaning to the word “cut”. From this description I only understand that a cut is the removal of an edge in a probabilistic graph, however there must/may be some arbitrariness in building the wrong conditional distribution. In the Poisson-binomial case treated in Martyn’s case, I interpret the cut as simulating from

$\pi(\phi|z)\pi(\theta|\phi,y)=\dfrac{\pi(\phi)f(z|\phi)}{m(z)}\dfrac{\pi(\theta|\phi)f(y|\theta,\phi)}{m(y|\phi)}$

$\pi(\phi|z,\mathbf{y})\pi(\theta|\phi,y)\propto\pi(\phi)f(z|\phi)\pi(\theta|\phi)f(y|\theta,\phi)$

hence loosing some of the information about φ… Now, this cut version is a function of φ and θ that can be fed to a Metropolis-Hastings algorithm. Assuming we can handle the posterior on φ and the conditional on θ given φ. If we build a Gibbs sampler instead, we face a difficulty with the normalising constant m(y|φ). Said Gibbs sampler thus does not work in generating from the “cut” target. Maybe an alternative borrowing from the rather large if disparate missing constant toolbox. (In any case, we do not simulate from the original joint distribution.) The natural solution would then be to make a independent proposal on φ with target the posterior given z and then any scheme that preserves the conditional of θ given φ and y; “any” is rather wistful thinking at this stage since the only practical solution that I see is to run a Metropolis-Hasting sampler long enough to “reach” stationarity… I also remain with a lingering although not life-threatening question of whether or not the BUGS code using cut distributions provide the “right” answer or not. Here are my five slides used during the seminar (with a random walk implementation that did not diverge from the true target…):

## MCMSki IV [day 3]

Posted in Mountains, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on January 9, 2014 by xi'an

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.

## MCMSki [day 2]

Posted in Mountains, pictures, Statistics, University life with tags , , , , , , , , , on January 8, 2014 by xi'an

I was still feeling poorly this morning with my brain in a kind of flu-induced haze so could not concentrate for a whole talk, which is a shame as I missed most of the contents of the astrostatistics session put together by David van Dyk… Especially the talk by Roberto Trotta I was definitely looking for. And the defence of nested sampling strategies for marginal likelihood approximations. Even though I spotted posterior distributions for WMAP and Plank data on the ΛCDM that reminded me of our own work in this area… Apologies thus to all speakers for dozing in and out, it was certainly not due to a lack of interest!

Sebastian Seehars mentioned emcee (for ensemble Monte Carlo), with a corresponding software nicknamed “the MCMC hammer”, and their own CosmoHammer software. I read the paper by Goodman and Ware (2010) this afternoon during the ski break (if not on a ski lift!). Actually, I do not understand why an MCMC should be affine invariant: a good adaptive MCMC sampler should anyway catch up the right scale of the target distribution. Other than that, the ensemble sampler reminds me very much of the pinball sampler we developed with Kerrie Mengersen (1995 Valencia meeting), where the target is the product of L targets,

$\pi(x_1)\cdots\pi(x_L)$

and a Gibbs-like sampler can be constructed, moving one component (with index k, say) of the L-sample at a time. (Just as in the pinball sampler.) Rather than avoiding all other components (as in the pinball sampler), Goodman and Ware draw a single other component at random  (with index j, say) and make a proposal away from it:

$\eta=x_j(t) + \zeta \{x_k(t)-x_j(t)\}$

where ζ is a scale random variable with (log-) symmetry around 1. The authors claim improvement over a single track Metropolis algorithm, but it of course depends on the type of Metropolis algorithms that is chosen… Overall, I think the criticism of the pinball sampler also applies here: using a product of targets can only slow down the convergence. Further, the affine structure of the target support is not a given. Highly constrained settings should not cope well with linear transforms and non-linear reparameterisations would be more efficient….