Both registration and call for papers have now been posted on the webpage of the 11th International Workshop on Objective Bayes Methodology, aka O-Bayes 15, that will take place in Valencia next June 1-5. The spectrum of the conference is quite wide, as reflected by the range of speakers. In addition, this conference is dedicated to our friend Susie Bayarri, to celebrate her life and contributions to Bayesian Statistics. And in continuation of the morning jog in the memory of George Casella organised by Laura Ventura in Padova, there will be a morning jog for Susie. So register for the meeting and bring your running shoes!
Archive for Padova
My friends Erlis Ruli, Nicola Sartori and Laura Ventura from Università degli Studi de Padova have just arXived a new paper entitled Approximate Bayesian Computation with composite score functions. While the paper provides a survey of composite likelihood methods, the core idea of the paper is to use the score function (of the composite likelihood) as the summary statistic,
when evaluated at the maximum composite likelihood at the observed data point. In the specific (but unrealistic) case of an exponential family, an ABC based on the score is asymptotically (i.e., as the tolerance ε goes to zero) exact. The choice of the composite likelihood thus induces a natural summary statistics and, as in our empirical likelihood paper, where we also use the score of a composite likelihood, the composite likelihoods that are available for computation are usually quite a few, thus leading to an automated choice of a summary statistic..
An interesting (common) feature in most examples found in this paper is that comparisons are made between ABC using the (truly) sufficient statistic and ABC based on the pairwise score function, which essentially relies on the very same statistics. So the difference, when there is a difference, pertains to the choice of a different combination of the summary statistics or, somehow equivalently to the choice of a different distance function. One of the examples starts from our MA(2) toy-example in the 2012 survey in Statistics and Computing. The composite likelihood is then based on the consecutive triplet marginal densities. As shown by the picture below, the composite version improves to some extent upon the original ABC solution using three autocorrelations.
A suggestion I would have about a refinement of the proposed method deals with the distance utilised in the paper, namely the sum of the absolute differences between the statistics. Indeed, this sum is not scaled at all, neither for regular ABC nor for composite ABC, while the composite likelihood perspective provides in addition to the score a natural metric through the matrix A(θ) [defined on page 12]. So I would suggest comparing the performances of the methods using instead this rescaling since, in my opinion and in contrast with a remark on page 13, it is relevant in some (many?) settings where the amount of information brought by the composite model widely varies from one parameter to the next.
Indeed, I liked the i-like workshop very much. Among the many interesting talks of the past two days (incl. Cristiano Varin’s ranking of Series B as the top influential stat. journal!) , Matti Vihola’s and Nicolas Chopin’s had the strongest impact on me (to the point of scribbling in my notebook). In a joint work with Christophe Andrieu, Matti focussed on evaluating the impact of replacing the target with an unbiased estimate in a Metropolis-Hastings algorithm. In particular, they found necessary and sufficient conditions for keeping geometric and uniform ergodicity. My question (asked by Iain Murray) was whether they had derived ways of selecting the number of terms in the unbiased estimator towards maximal efficiency. I also wonder if optimal reparameterisations can be found in this sense (since unbiased estimators remain unbiased after reparameterisation).
Nicolas’ talk was about particle Gibbs sampling, a joint paper with Sumeet Singh recently arXived. I did not catch the whole detail of their method but/as I got intrigued by a property of Marc Beaumont’s algorithm (the very same algorithm used by Matti & Christophe). Indeed, the notion is that an unbiased estimator of the target distribution can be found in missing variable settings by picking an importance sampling distribution q on those variables. This representation leads to a pseudo-target Metropolis-Hastings algorithm. In the stationary regime, there exists a way to derive an “exact” simulation from the joint posterior on (parameter,latent). All the remaining/rejected latents are then distributed from the proposal q. What I do not see is how this impacts the next MCMC move since it implies generating a new sample of latent variables. I spoke with Nicolas about this over breakfast: the explanation is that this re-generated set of latent variables can be used in the denominator of the Metropolis-Hastings acceptance probability and is validated as a Gibbs step. (Incidentally, it may be seen as a regeneration event as well.)
Furthermore, I had a terrific run in the rising sun (at 5am) all the way to Kenilworth where I saw a deer, pheasants and plenty of rabbits. (As well as this sculpture that now appears to me as being a wee sexist…)
While in Montpellier yesterday (where I also had the opportunity of tasting an excellent local wine!), I had a look at the 1992 Biometrika paper by Monahan and Boos on “Proper likelihoods for Bayesian analysis“. This is a paper I missed and that was pointed out to me during the discussions in Padova. The main point of this short paper is to decide when a method based on an approximative likelihood function is truly (or properly) Bayes. Just the very question a bystander would ask of ABC methods, wouldn’t it?! The validation proposed by Monahan and Boos is one of calibration of credible sets, just as in the recent arXiv paper of Dennis Prangle, Michael Blum, G. Popovic and Scott Sisson I reviewed three months ago. The idea is indeed to check by simulation that the true posterior coverage of an α-level set equals the nominal coverage α. In other words, the predictive based on the likelihood approximation should be uniformly distributed and this leads to a goodness-of-fit test based on simulations. As in our ABC model choice paper, Proper likelihoods for Bayesian analysis notices that Bayesian inference drawn upon an insufficient statistic is proper and valid, simply less accurate than the Bayesian inference drawn upon the whole dataset. The paper also enounces a conjecture:
A [approximate] likelihood L is a coverage proper Bayesian likelihood if and inly if L has the form L(y|θ) = c(s) g(s|θ) where s=S(y) is a statistic with density g(s|θ) and c(s) some function depending on s alone.
conjecture that sounds incorrect in that noisy ABC is also well-calibrated. (I am not 100% sure of this argument, though.) An interesting section covers the case of pivotal densities as substitute likelihoods and of the confusion created by the double meaning of the parameter θ. The last section is also connected with ABC in that Monahan and Boos reflect on the use of large sample approximations, like normal distributions for estimates of θ which are a special kind of statistics, but do not report formal results on the asymptotic validation of such approximations. All in all, a fairly interesting paper!
Reading this highly interesting paper also made me realise that the criticism I had made in my review of Prangle et al. about the difficulty for this calibration method to address the issue of summary statistics was incorrect: when using the true likelihood function, the use of an arbitrary summary statistics is validated by this method and is thus proper.