## ABC with kernelised regression

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on February 22, 2017 by xi'an

The exact title of the paper by Jovana Metrovic, Dino Sejdinovic, and Yee Whye Teh is DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. It appeared last year in the proceedings of ICML.  The idea is to build ABC summaries by way of reproducing kernel Hilbert spaces (RKHS). Regressing such embeddings to the “optimal” choice of summary statistics by kernel ridge regression. With a possibility to derive summary statistics for quantities of interest rather than for the entire parameter vector. The use of RKHS reminds me of Arthur Gretton’s approach to ABC, although I see no mention made of that work in the current paper.

In the RKHS pseudo-linear formulation, the prediction of a parameter value given a sample attached to this value looks like a ridge estimator in classical linear estimation. (I thus wonder at why one would stop at the ridge stage instead of getting the full Bayes treatment!) Things get a bit more involved in the case of parameters (and observations) of interest, as the modelling requires two RKHS, because of the conditioning on the nuisance observations. Or rather three RHKS. Since those involve a maximum mean discrepancy between probability distributions, which define in turn a sort of intrinsic norm, I also wonder at a Wasserstein version of this approach.

What I find hard to understand in the paper is how a large-dimension large-size sample can be managed by such methods with no visible loss of information and no explosion of the computing budget. The authors mention Fourier features, which never rings a bell for me, but I wonder how this operates in a general setting, i.e., outside the iid case. The examples do not seem to go into enough details for me to understand how this massive dimension reduction operates (and they remain at a moderate level in terms of numbers of parameters). I was hoping Jovana Mitrovic could present her work here at the 17w5025 workshop but she sadly could not make it to Banff for lack of funding!

Posted in Statistics, University life with tags , , , , , , , , , , , , on November 15, 2012 by xi'an

Following in the reading classics series, my Master students in the Reading Classics Seminar course, listened today to Kaniav Kamary analysis of Denis Lindley’s and Adrian Smith’s 1972 linear Bayes paper Bayes Estimates for the Linear Model in JRSS Series B. Here are her (Beamer) slides

At a first (mathematical) level this is an easier paper in the list, because it relies on linear algebra and normal conditioning. Of course, this is not the reason why Bayes Estimates for the Linear Model is in the list and how it impacted the field. It is indeed one of the first expositions on hierarchical Bayes programming, with some bits of empirical Bayes shortcuts when computation got a wee in the way. (Remember, this is 1972, when shrinkage estimation and its empirical Bayes motivations is in full blast…and—despite Hstings’ 1970 Biometrika paper—MCMC is yet to be imagined, except maybe by Julian Besag!) So, at secondary and tertiary levels, it is again hard to discuss, esp. with Kaniav’s low fluency in English. For instance, a major concept in the paper is exchangeability, not such a surprise given Adrian Smith’s translation of de Finetti into English. But this is a hard concept if only looking at the algebra within the paper, as a motivation for exchangeability and partial exchangeability (and hierarchical models) comes from applied fields like animal breeding (as in Sørensen and Gianola’s book). Otherwise, piling normal priors on top of normal priors is lost on the students. An objection from a 2012 reader is also that the assumption of exchangeability on the parameters of a regression model does not really make sense when the regressors are not normalised (this is linked to yesterday’s nefarious post!): I much prefer the presentation we make of the linear model in Chapter 3 of our Bayesian Core. Based on Arnold Zellner‘s g-prior. An interesting question from one student was whether or not this paper still had any relevance, other than historical. I was a bit at a loss on how to answer as, again, at a first level, the algebra was somehow natural and, at a statistical level, less informative priors could be used. However, the idea of grouping parameters together in partial exchangeability clusters remained quite appealing and bound to provide gains in precision….

Posted in Statistics, University life with tags , , , , , , , , , , , on November 8, 2012 by xi'an

Following last week read of Hartigan and Wong’s 1979 K-Means Clustering Algorithm, my Master students in the Reading Classics Seminar course, listened today to Agnė Ulčinaitė covering Rob Tibshirani‘s original LASSO paper Regression shrinkage and selection via the lasso in JRSS Series B. Here are her (Beamer) slides

Again not the easiest paper in the list, again mostly algorithmic and requiring some background on how it impacted the field. Even though Agnė also went through the Elements of Statistical Learning by Hastie, Friedman and Tibshirani, it was hard to get away from the paper to analyse more widely the importance of the paper, the connection with the Bayesian (linear) literature of the 70’s, its algorithmic and inferential aspects, like the computational cost, and the recent extensions like Bayesian LASSO. Or the issue of handling n<p models. Remember that one of the S in LASSO stands for shrinkage: it was quite pleasant to hear again about ridge estimators and Stein’s unbiased estimator of the risk, as those were themes of my Ph.D. thesis… (I hope the students do not get discouraged by the complexity of those papers: there were fewer questions and fewer students this time. Next week, the compass will move to the Bayesian pole with a talk on Lindley and Smith’s 1973 linear Bayes paper by one of my PhD students.)

## dimension reduction in ABC [a review’s review]

Posted in Statistics, University life with tags , , , , , , , , , , , on February 27, 2012 by xi'an

What is very apparent from this study is that there is no single `best’ method of dimension reduction for ABC.

Michael Blum, Matt Nunes, Dennis Prangle and Scott Sisson just posted on arXiv a rather long review of dimension reduction methods in ABC, along with a comparison on three specific models. Given that the choice of the vector of summary statistics is presumably the most important single step in an ABC algorithm and as selecting too large a vector is bound to fall victim of the dimension curse, this is a fairly relevant review! Therein, the authors compare regression adjustments à la Beaumont et al.  (2002), subset selection methods, as in Joyce and Marjoram (2008), and projection techniques, as in Fearnhead and Prangle (2012). They add to this impressive battery of methods the potential use of AIC and BIC. (Last year after ABC in London I reported here on the use of the alternative DIC by Francois and Laval, but the paper is not in the bibliography, I wonder why.) An argument (page 22) for using AIC/BIC is that either provides indirect information about the approximation of p(θ|y) by p(θ|s); this does not seem obvious to me.

The paper also suggests a further regularisation of Beaumont et al.  (2002) by ridge regression, although L1 penalty à la Lasso would be more appropriate in my opinion for removing extraneous summary statistics. (I must acknowledge never being a big fan of ridge regression, esp. in the ad hoc version à la Hoerl and Kennard, i.e. in a non-decision theoretic approach where the hyperparameter λ is derived from the data by X-validation, since it then sounds like a poor man’s Bayes/Stein estimate, just like BIC is a first order approximation to regular Bayes factors… Why pay for the copy when you can afford the original?!) Unsurprisingly, ridge regression does better than plain regression in the comparison experiment when there are many almost collinear summary statistics, but an alternative conclusion could be that regression analysis is not that appropriate with  many summary statistics. Indeed, summary statistics are not quantities of interest but data summarising tools towards a better approximation of the posterior at a given computational cost… (I do not get the final comment, page 36, about the relevance of summary statistics for MCMC or SMC algorithms: the criterion should be the best approximation of p(θ|y) which does not depend on the type of algorithm.)

I find it quite exciting to see the development of a new range of ABC papers like this review dedicated to a better derivation of summary statistics in ABC, each with different perspectives and desideratas, as it will help us to understand where ABC works and where it fails, and how we could get beyond ABC…