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Bayesian Model Choice in Cumulative Link Ordinal Regression Models

Trevelyan J. McKinley, Michelle Morters and James L. N. Wood
Categories: Bayesian Analysis

Informative g-Priors for Logistic Regression

Timothy E. Hanson, Adam J. Branscum, Wesley O. Johnson
Categories: Bayesian Analysis

Cluster Analysis, Model Selection, and Prior Distributions on Models

George Casella, Elias Moreno and F.J. Giron
Categories: Bayesian Analysis

Toward Rational Social Decisions: A Review and Some Results

Joseph B. Kadane and Steven N. MacEachern
Categories: Bayesian Analysis

Adaptive Priors based on Splines with Random Knots

Eduard Belitser and Paulo Serra
Categories: Bayesian Analysis

Stratified Graphical Models - Context-Specific Independence in Graphical Models

Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander
Categories: Bayesian Analysis

recycling accept-reject rejections

Xian's Og - Mon, 2014-06-30 18:14

Vinayak Rao, Lizhen Lin and David Dunson just arXived a paper which proposes anew technique to handle intractable normalising constants. And which exact title is Data augmentation for models based on rejection sampling. (Paper that I read in the morning plane to B’ham, since this is one of my weeks in Warwick.) The central idea therein is that, if the sample density (aka likelihood) satisfies

where all terms but p are known in closed form, then completion by the rejected values of an hypothetical accept-reject algorithm−hypothetical in the sense that the data does not have to be produced by an accept-reject scheme but simply the above domination condition to hold−allows for a data augmentation scheme. Without requiring the missing normalising constant. Since the completed likelihood is

A closed-form, if not necessarily congenial, function.

Now this is quite a different use of the “rejected values” from the accept reject algorithm when compared with our 1996 Biometrika paper on the Rao-Blackwellisation of accept-reject schemes (which, still, could have been mentioned there… Or Section 4.2 of Monte Carlo Statistical Methods. Rather than re-deriving the joint density of the augmented sample, “accepted+rejected”.)

It is a neat idea in that it completely bypasses the approximation of the normalising constant. And avoids the somewhat delicate tuning of the auxiliary solution of Moller et al. (2006)  The difficulty with this algorithm is however in finding an upper bound M on the unnormalised density f that is

  1. in closed form;
  2. with a manageable and tight enough “constant” M;
  3. compatible with running a posterior simulation conditional on the added rejections.

The paper seems to assume further that the bound M is independent from the current parameter value θ, at least as suggested by the notation (and Theorem 2), but this is not in the least necessary for the validation of the formal algorithm. Such a constraint would pull M higher, hence reducing the efficiency of the method. Actually the matrix Langevin distribution considered in the first example involves a bound that depends on the parameter κ.

The paper includes a result (Theorem 2) on the uniform ergodicity that relies on heavy assumptions on the proposal distribution. And a rather surprising one, namely that the probability of rejection is bounded from below, i.e. calling for a less efficient proposal. Now it seems to me that a uniform ergodicity result holds as well when the probability of acceptance is bounded from below since, then, the event when no rejection occurs constitutes an atom from the augmented Markov chain viewpoint. There therefore occurs a renewal each time the rejected variable set ϒ is empty, and ergodicity ensues (Robert, 1995, Statistical Science).

Note also that, despite the opposition raised by the authors, the method per se does constitute a pseudo-marginal technique à la Andrieu-Roberts (2009) since the independent completion by the (pseudo) rejected variables produces an unbiased estimator of the likelihood. It would thus be of interest to see how the recent evaluation tools of Andrieu and Vihola can assess the loss in efficiency induced by this estimation of the likelihood.

Maybe some further experimental evidence tomorrow…

Filed under: Statistics, University life Tagged: accept-reject algorithm, arXiv, auxiliary variable, Data augmentation, George Casella, intractable likelihood, Monte Carlo Statistical Methods, Rao-Blackwellisation, recycling, untractable normalizing constant
Categories: Bayesian Bloggers

R/Rmetrics in Paris [alas!]

Xian's Og - Sun, 2014-06-29 18:14

Today I gave a talk on Bayesian model choice in a fabulous 13th Century former monastery in the Latin Quarter of Paris… It is the Collège des Bernardins, close to Jussieu and Collège de France, unbelievably hidden to the point I was not aware of its existence despite having studied and worked in Jussieu since 1982… I mixed my earlier San Antonio survey on importance sampling approximations to Bayes factors with an entry to our most recent work on ABC with random forests. This was the first talk of the 8th R/Rmetrics workshop taking place in Paris this year. (Rmetrics is aiming at aggregating R packages with econometrics and finance applications.) And I had a full hour and a half to deliver my lecture to the workshop audience. Nice place, nice people, new faces and topics (and even andouille de Vire for lunch!): why should I complain with an alas in the title?!What happened is that the R/Rmetrics meetings have been till this year organised in Meielisalp, Switzerland. Which stands on top of Thuner See and… just next to the most famous peaks of the Bernese Alps! And that I had been invited last year but could not make it… Meaning I lost a genuine opportunity to climb one of my five dream routes, the Mittelegi ridge of the Eiger. As the future R/Rmetrics meetings will not take place there.

A lunch discussion at the workshop led me to experiment the compiler library in R, library that I was unaware of. The impact on the running time is obvious: recycling the fowler function from the last Le Monde puzzle,

> bowler=cmpfun(fowler) > N=20;n=10;system.time(fowler(pred=N)) user system elapsed 52.647 0.076 56.332 > N=20;n=10;system.time(bowler(pred=N)) user system elapsed 51.631 0.004 51.768 > N=20;n=15;system.time(bowler(pred=N)) user system elapsed 51.924 0.024 52.429 > N=20;n=15;system.time(fowler(pred=N)) user system elapsed 52.919 0.200 61.960

shows a ten- to twenty-fold gain in system time, if not in elapsed time (re-alas!).

Filed under: Mountains, pictures, R, Statistics, Travel, University life Tagged: ABC, andouille de Vire, Bayesian econometrics, Bayesian model choice, Bernese Alps, cmpfun(), Collège des Bernardins, compiler, Eiger, importance sampling, Interlaken, Meielisalp, Mittelegi ridge, Paris, R, Rmetrics, San Antonio, Switzerland, system.time, Thun Lake
Categories: Bayesian Bloggers

…et sinon Mr Cardoso réussit là où les ont échoué!

Xian's Og - Sun, 2014-06-29 08:18

A similar flier, a few days later. With very precise (if incoherent) guarantees! And a fantastic use of capitals. Too bad Monsieur Cardoso could not predict the (occurrence of a) missing noun in the last sentence…

Filed under: Kids, pictures Tagged: advertising, charlatanism, mailbox, Pantin, Paris
Categories: Bayesian Bloggers

Prof. Ntayiya résout tous vos problèmes!

Xian's Og - Sat, 2014-06-28 18:14

One of the numerous fliers for “occult expertise” that end up in my mailbox… An interesting light on the major woes of my neighbours. That can induce some to consult with such charlatans. And a wonder: how can Professeur Ntayiya hope to get paid if the effects need to be proven?!

Filed under: pictures Tagged: black block, charlatanism, fliers, Fontenay-aux-Roses, mailbox
Categories: Bayesian Bloggers