Michael Betancourt found this street name in London and used it for his talk in Seattle. Even though he should have photoshopped the dead end symbol, which begged for my sarcastic comment during the talk…
Archive for the Books Category
In Roissy (De Gaulle) airport, prior to catching my flight to Seattle, I noticed a “new” Indriðason‘s novel, Le Duel (Einvígið), that has not yet been translated into English. But just translated into French! This is a most unusual novel in the Erlendur series, in that the central character of the series only appears as a young cop in the final lines of the novel. Instead, the mentor of Erlendur, Marion Biem, is conducting an inquiry as to who had killed a young man in an almost empty Reykjavik cinema. Where almost all spectators seemed to have something to hide, if not always a murder… A classical whodunnit?! Not really because this happens in 1972, during the famous Fisher-Spassky duel, and that duel is unrelated to the murder, while the Icelandic police seems overwrought by the event and the presence of Russian and American double-agents in Reykjavik…
I found the whole exercise interesting, creating a sort of genealogy in the Erlendur series, with Marion’s mentor playing a side role and his early training in Glasgow (of all places!), with the re-creation of a 1972 Iceland and the chess match between Fisher and Spassky at the height of the Cold War. Plus a reminder about the tuberculosis epidemics of the 1930’s, where The detective side of the novel is however less convincing than usual, with clues and fingerprints appearing at the most convenient times. And a fairly convoluted resolution. Still worth reading, especially on a long flight!
I cannot really remember how I came across this book, when selecting Amazon (free) books to collect from Andrew on my last trip to New York… (Thanks to ‘Og readers!) Presumably the name popped out of a list of recommended books. The cover was intriguing enough to stop by and to spot that the author was Elizabeth Hand, whose horror/fantasy trilogy I had liked very much in the late 80’s… So I ordered the book and brought it back from New York. Only to realise that this was an altogether different Elizabeth Hand, whose book Available Dark I had read a little while ago. And did not like so much. However, since the book is a collection of short and less short stories, I gave it a try.
As it happens, this Saffron and Brimstone truly is a great collection of short stories, fantastic in a completely different frame than those of the fantasy books I usually review here. It is a fantastic that borders reality, sometimes hardly fantastic, but with a constant feeling of something being not fully natural, not completely normal. The subtitle of “strange stories” is quite pertinent, as the feeling of strangeness hits the reader (or this reader) almost instantaneously from the beginning of each story. I enjoyed all of the eight stories for different reasons, from a reminiscence of an “Alfred Hitchcock presents” short story called the Cocoon that terrified me [as a pre-teen] when I read it late at night!, to variations around Greek myths that brings them beautifully into the modern era. And always with a central female character who brings another degree of strangeness and surreality to the tale. I do not think it matters the least that those novels are or are not fantasy or fantastic. They are simply gems of contemporary literature. Superb. (Which makes the rather unexceptional Available Dark the more inexplicable!)
My first morning session was about inference for philogenies. While I was expecting some developments around Kingman’s coalescent models my coauthors needed and developped ABC for, I was surprised to see models that were producing closed form (or close enough to) likelihoods. Due to strong restrictions on the population sizes and migration possibilities, as explained later to me by Vladimir Minin. No need for ABC there since MCMC was working on the species trees, with Vladimir Minin making use of [the Savage Award winner] Vinayak Rao’s approach on trees that differ from the coalescent. And enough structure to even consider and demonstrate tree identifiability in Laura Kubatko’s case.
I then stopped by the astrostatistics session as the first talk by Gwendolin Eddie was about galaxy mass estimation, a problem I may actually be working on in the Fall, but it ended up being a completely different problem and I was further surprised that the issue of whether or not the data was missing at random was not considered by the authors.
Christening a session Unifying foundation(s) may be calling for trouble, at least from me! In this spirit, Xiao Li Meng gave a talk attempting at a sort of unification of the frequentist, Bayesian, and fiducial paradigms by introducing the notion of personalized inference, which is a notion I had vaguely thought of in the past. How much or how far do you condition upon? However, I have never thought of this justifying fiducial inference in any way and Xiao Li’s lively arguments during and after the session not enough to convince me of the opposite: Prior-free does not translate into (arbitrary) choice-free. In the earlier talk about confidence distributions by Regina Liu and Minge Xie, that I partly missed for Galactic reasons, I just entered into the room at the very time when ABC was briefly described as a confidence distribution because it was not producing a convergent approximation to the exact posterior, a logic that escapes me (unless those confidence distributions are described in such a loose way as to include about any method f inference). Dongchu Sun also gave us a crash course on reference priors, with a notion of random posteriors I had not heard of before… As well as constructive posteriors… (They seemed to mean constructible matching priors as far as I understood.)
The final talk in this session by Chuanhei Liu on a new approach (modestly!) called inferential model was incomprehensible, with the speaker repeatedly stating that the principles were too hard to explain in five minutes and needed an incoming book… I later took a brief look at an associated paper, which relates to fiducial inference and to Dempster’s belief functions. For me, it has the same Münchhausen feeling of creating a probability out of nothing, creating a distribution on the parameter by ignoring the fact that the fiducial equation x=a(θ,u) modifies the distribution of u once x is observed.
.[Following my posting of a misfiled 2013 blog, Ewan Cameron told me of the impact of this paper in starting his own blog and I asked him for a guest post, resulting in this analysis, much deeper than mine. No warning necessary this time!]
Back in February 2013 when “Bayesian Model Averaging in Astrophysics: A Review” by Parkinson & Liddle (hereafter PL13) first appeared on the arXiv I was a keen, young(ish) postdoc eager to get stuck into debates about anything and everything ‘astro-statistical’. And with its seemingly glaring flaws, PL13 was more grist to the mill. However, despite my best efforts on various forums I couldn’t get a decent fight started over the right way to do model averaging (BMA) in astronomy, so out of sheer frustration two months later I made my own soapbox to shout from at Another Astrostatistics Blog. Having seen PL13 reviewed recently here on Xian’s Og it feels like the right time to revisit the subject and reflect on where BMA in astronomy is today.
As pointed out to me back in 2013 by Tom Loredo, the act of Bayesian model averaging has been around much longer than its name; indeed an early astronomical example appears in Gregory & Loredo (1992) in which the posterior mean representation of an unknown signal is constructed for an astronomical “light-curve”, averaging over a set of constant and periodic candidate models. Nevertheless the wider popularisation of model averaging in astronomy has only recently taken place through a variety of applications in cosmology: e.g. Liddle, Mukherjee, Parkinson & Wang (2006) and Vardanyan, Trotta & Silk (2011).
In contrast to earlier studies like Gregory & Loredo (1992)—or the classic review on BMA by Hoeting et al. (1999)—in which the target of model averaging is typically either a utility function, a set of future observations, or a latent parameter of the observational process (e.g. the unknown “light-curve” shape) shared naturally by all competing models, the proposal of cosmological BMA studies is to produce a model-averaged version of the posterior for a given ‘shared’ parameter: a so-called “model-averaged PDF”. This proposal didn’t sit well with me back in 2013, and it still doesn’t sit well with me today. Philosophically: without a model a parameter has no meaning, so why should we want to seek meaning in the marginalised distribution of a parameter over an entire set of models? And, practically: to put it another way, without knowing the model ‘label’ to which a given mass of model-averaged parameter probability belongs there’s nothing much useful we can do with this ‘PDF’: nothing much we can say about the data we’ve just analysed and nothing much we can say about future experiments. Whereas the space of the observed data is shared automatically by all competing models, it seems to me to be somehow “un-Bayesian” to place the further restriction that the parameters of separate models share the same scale and topology. I say “un-Bayesian” since this mode of model averaging suggests a formulation of the parameter space + prior pairing stronger than the statement of one’s prior beliefs for the distribution of observable data given the model. But I would be happy to hear arguments from the other side in the comments box below … ! Continue reading
Today, at JSM 2015, in Seattle, I attended several Bayesian sessions, having sadly missed the Dennis Lindley memorial session yesterday, as it clashed with my own session. In the morning sessions on Bayesian model choice, David Rossell (Warwick) defended non-local priors à la Johnson (& Rossell) as having better frequentist properties. Although I appreciate the concept of eliminating a neighbourhood of the null in the alternative prior, even from a Bayesian viewpoint since it forces us to declare explicitly when the null is no longer acceptable, I find the asymptotic motivation for the prior less commendable and open to arbitrary choices that may lead to huge variations in the numerical value of the Bayes factor. Another talk by Jin Wang merged spike and slab with EM with bootstrap with random forests in variable selection. But I could not fathom what the intended properties of the method were… Besides returning another type of MAP.
The second Bayesian session of the morn was mostly centred on sparsity and penalisation, with Carlos Carvalho and Rob McCulloch discussing a two step method that goes through a standard posterior construction on the saturated model, before using a utility function to select the pertinent variables. Separation of utility from prior was a novel concept for me, if not for Jay Kadane who objected to Rob a few years ago that he put in the prior what should be in the utility… New for me because I always considered the product prior x utility as the main brick in building the Bayesian edifice… Following Herman Rubin’s motto! Veronika Rocková linked with this post-LASSO perspective by studying spike & slab priors based on Laplace priors. While Veronicka’s goal was to achieve sparsity and consistency, this modelling made me wonder at the potential equivalent in our mixtures for testing approach. I concluded that having a mixture of two priors could be translated in a mixture over the sample with two different parameters, each with a different prior. A different topic, namely multiple testing, was treated by Jim Berger, who showed convincingly in my opinion that a Bayesian approach provides a significant advantage.
In the afternoon finalists of the ISBA Savage Award presented their PhD work, both in the theory and methods section and in the application section. Besides Veronicka Rocková’s work on a Bayesian approach to factor analysis, with a remarkable resolution via a non-parametric Indian buffet prior and a variable selection interpretation that avoids MCMC difficulties, Vinayak Rao wrote his thesis on MCMC methods for jump processes with a finite number of observations, using a highly convincing completion scheme that created independence between blocks and which reminded me of the Papaspiliopoulos et al. (2005) trick for continuous time processes. I do wonder at the potential impact of this method for processing the coalescent trees in population genetics. Two talks dealt with inference on graphical models, Masanao Yajima and Christine Peterson, inferring the structure of a sparse graph by Bayesian methods. With applications in protein networks. And with again a spike & slab prior in Christine’s work. The last talk by Sayantan Banerjee was connected to most others in this Savage session in that it also dealt with sparsity. When estimating a large covariance matrix. (It is always interesting to try to spot tendencies in awards and conferences. Following the Bayesian non-parametric era, are we now entering the Bayesian sparsity era? We will see if this is the case at ISBA 2016!) And the winner is..?! We will know tomorrow night! In the meanwhile, congrats to my friends Sudipto Banerjee, Igor Prünster, Sylvia Richardson, and Judith Rousseau who got nominated IMS Fellows tonight.
This afternoon, at JSM 2015, in Seattle, we had the Bayesian Computation I and II sessions that Omiros Papaspiliopoulos and myself put together (sponsored by IMS and ISBA). Despite this being Sunday and hence having some of the participants still arriving, the sessions went on well in terms of audience. Thanks to Mark Girolami’s strict presidency, we were so much on time in Bayesian Computation I that we had 20mn left for a floor discussion that turned into a speakers’ discussion! All talks were of obvious interest for MCMCists, but Ryan Adams’ presentation on firefly Monte Carlo got me thinking for most of the afternoon on different ways of exploiting the existence of a bound on the terms composing the target. With little to show by the end of the afternoon! On the mundane side, I was sorry to miss Pierre Jacob, who was still in France due to difficulties in obtaining a working visa for Harvard (!), and surprised to see Dawn Woodard wearing a Uber tee-shirt, until she told us she was now working at Uber! Which a posteriori makes sense, given her work on traffic predictions!