Archive for finite mixtures

Challis Lectures

Posted in Books, pictures, Statistics, Travel, University life, Wines with tags , , , , , , , on November 23, 2014 by xi'an

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I had a great time during this short visit in the Department of Statistics, University of Florida, Gainesville. First, it was a major honour to be the 2014 recipient of the George H. Challis Award and I considerably enjoyed delivering my lectures on mixtures and on ABC with random forests, And chatting with members of the audience about the contents afterwards. Here is the physical award I brought back to my office:

Challis

More as a piece of trivia, here is the amount of information about the George H. Challis Award I found on the UF website:

This fund was established in 2000 by Jack M. and Linda Challis Gill and the Gill Foundation of Texas, in memory of Linda’s father, to support faculty and student conference travel awards and the George Challis Biostatistics Lecture Series. George H. Challis was born on December 8, 1911 and was raised in Italy and Indiana. He was the first cousin of Indiana composer Cole Porter. George earned a degree in 1933 from the School of Business at Indiana University in Bloomington. George passed away on May 6, 2000. His wife, Madeline, passed away on December 14, 2009.

Cole Porter, indeed!

On top of this lecturing activity, I had a full academic agenda, discussing with most faculty members and PhD students of the Department, on our respective research themes over the two days I was there and it felt like there was not enough time! And then, during the few remaining hours where I did not try to stay on French time (!), I had a great time with my friends Jim and Maria in Gainesville, tasting a fantastic local IPA beer from Cigar City Brewery and several great (non-local) red wines… Adding to that a pile of new books, a smooth trip both ways, and a chance encounter with Alicia in Atlanta airport, it was a brilliant extended weekend!

importance sampling schemes for evidence approximation [revised]

Posted in Statistics, University life with tags , , , , , , , on November 18, 2014 by xi'an

After a rather intense period of new simulations and versions, Juong Een (Kate) Lee and I have now resubmitted our paper on (some) importance sampling schemes for evidence approximation in mixture models to Bayesian Analysis. There is no fundamental change in the new version but rather a more detailed description of what those importance schemes mean in practice. The original idea in the paper is to improve upon the Rao-Blackwellisation solution proposed by Berkoff et al. (2002) and later by Marin et al. (2005) to avoid the impact of label switching on Chib’s formula. The Rao-Blackwellisation consists in averaging over all permutations of the labels while the improvement relies on the elimination of useless permutations, namely those that produce a negligible conditional density in Chib’s (candidate’s) formula. While the improvement implies truncated the overall sum and hence induces a potential bias (which was the concern of one referee), the determination of the irrelevant permutations after relabelling next to a single mode does not appear to cause any bias, while reducing the computational overload. Referees also made us aware of many recent proposals that conduct to different evidence approximations, albeit not directly related with our purpose. (One was Rodrigues and Walker, 2014, discussed and commented in a recent post.)

back in Gainesville (FL)

Posted in pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , on November 12, 2014 by xi'an

 

Today, I am flying to Gainesville, Florida, for the rest of the week, to give a couple of lectures. More precisely, I have actually been nominated the 2014 Challis lecturer by the Department of Statistics there, following an impressive series of top statisticians (most of them close friends, is there a correlation there?!). I am quite excited to meet again with old friends and to be back at George’s University, if only for a little less than three days. (There is a certain trend in those Fall trips as I have been going for a few days and two talks to the USA or Canada for the past three Falls: to Ames and Chicago in 2012, to Pittsburgh (CMU) and Toronto in 2013…)

label switching in Bayesian mixture models

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on October 31, 2014 by xi'an

cover of Mixture Estimation and ApplicationsA referee of our paper on approximating evidence for mixture model with Jeong Eun Lee pointed out the recent paper by Carlos Rodríguez and Stephen Walker on label switching in Bayesian mixture models: deterministic relabelling strategies. Which appeared this year in JCGS and went beyond, below or above my radar.

Label switching is an issue with mixture estimation (and other latent variable models) because mixture models are ill-posed models where part of the parameter is not identifiable. Indeed, the density of a mixture being a sum of terms

\sum_{j=1}^k \omega_j f(y|\theta_i)

the parameter (vector) of the ω’s and of the θ’s is at best identifiable up to an arbitrary permutation of the components of the above sum. In other words, “component #1 of the mixture” is not a meaningful concept. And hence cannot be estimated.

This problem has been known for quite a while, much prior to EM and MCMC algorithms for mixtures, but it is only since mixtures have become truly estimable by Bayesian approaches that the debate has grown on this issue. In the very early days, Jean Diebolt and I proposed ordering the components in a unique way to give them a meaning. For instant, “component #1″ would then be the component with the smallest mean or the smallest weight and so on… Later, in one of my favourite X papers, with Gilles Celeux and Merrilee Hurn, we exposed the convergence issues related with the non-identifiability of mixture models, namely that the posterior distributions were almost always multimodal, with a multiple of k! symmetric modes in the case of exchangeable priors, and therefore that Markov chains would have trouble to visit all those modes in a symmetric manner, despite the symmetry being guaranteed from the shape of the posterior. And we conclude with the slightly provocative statement that hardly any Markov chain inferring about mixture models had ever converged! In parallel, time-wise, Matthew Stephens had completed a thesis at Oxford on the same topic and proposed solutions for relabelling MCMC simulations in order to identify a single mode and hence produce meaningful estimators. Giving another meaning to the notion of “component #1″.

And then the topic began to attract more and more researchers, being both simple to describe and frustrating in its lack of definitive answer, both from simulation and inference perspectives. Rodriguez’s and Walker’s paper provides a survey on the label switching strategies in the Bayesian processing of mixtures, but its innovative part is in deriving a relabelling strategy. Which consists of finding the optimal permutation (at each iteration of the Markov chain) by minimising a loss function inspired from k-means clustering. Which is connected with both Stephens’ and our [JASA, 2000] loss functions. The performances of this new version are shown to be roughly comparable with those of other relabelling strategies, in the case of Gaussian mixtures. (Making me wonder if the choice of the loss function is not favourable to Gaussian mixtures.) And somehow faster than Stephens’ Kullback-Leibler loss approach.

“Hence, in an MCMC algorithm, the indices of the parameters can permute multiple times between iterations. As a result, we cannot identify the hidden groups that make [all] ergodic averages to estimate characteristics of the components useless.”

One section of the paper puzzles me, albeit it does not impact the methodology and the conclusions. In Section 2.1 (p.27), the authors consider the quantity

p(z_i=j|{\mathbf y})

which is the marginal probability of allocating observation i to cluster or component j. Under an exchangeable prior, this quantity is uniformly equal to 1/k for all observations i and all components j, by virtue of the invariance under permutation of the indices… So at best this can serve as a control variate. Later in Section 2.2 (p.28), the above sentence does signal a problem with those averages but it seem to attribute it to MCMC behaviour rather than to the invariance of the posterior (or to the non-identifiability of the components per se). At last, the paper mentions that “given the allocations, the likelihood is invariant under permutations of the parameters and the allocations” (p.28), which is not correct, since eqn. (8)

f(y_i|\theta_{\sigma(z_i)}) =f(y_i|\theta_{\tau(z_i)})

does not hold when the two permutations σ and τ give different images of zi

Jeffreys prior with improper posterior

Posted in Books, Statistics, University life with tags , , , , , , , , , , on May 12, 2014 by xi'an

In a complete coincidence with my visit to Warwick this week, I became aware of the paper “Inference in two-piece location-scale models with Jeffreys priors” recently published in Bayesian Analysis by Francisco Rubio and Mark Steel, both from Warwick. Paper where they exhibit a closed-form Jeffreys prior for the skewed distribution

\dfrac{2\epsilon}{\sigma_1}f(\{x-\mu\}/\sigma_1)\mathbb{I}_{x<\mu}+\dfrac{2(1-\epsilon)}{\sigma_2}f(\{x-\mu\}/\sigma_2) \mathbb{I}_{x>\mu}

where f is a symmetric density, namely

\pi(\mu,\sigma_1,\sigma_2) \propto 1 \big/ \sigma_1\sigma_2\{\sigma_1+\sigma_2\}\,,

where

\epsilon=\sigma_1/\{\sigma_1+\sigma_2\}\,.

only to show  immediately after that this prior does not allow for a proper posterior, no matter what the sample size is. While the above skewed distribution can always be interpreted as a mixture, being a weighted sum of two terms, it is not strictly speaking a mixture, if only because the “component” can be identified from the observation (depending on which side of μ is stands). The likelihood is therefore a product of simple terms rather than a product of a sum of two terms.

As a solution to this conundrum, the authors consider the alternative of the “independent Jeffreys priors”, which are made of a product of conditional Jeffreys priors, i.e., by computing the Jeffreys prior one parameter at a time with all other parameters considered to be fixed. Which differs from the reference prior, of course, but would have been my second choice as well. Despite criticisms expressed by José Bernardo in the discussion of the paper… The difficulty (in my opinion) resides in the choice (and difficulty) of the parameterisation of the model, since those priors are not parameterisation-invariant. (Xinyi Xu makes the important comment that even those priors incorporate strong if hidden information. Which relates to our earlier discussion with Kaniav Kamari on the “dangers” of prior modelling.)

Although the outcome is puzzling, I remain just slightly sceptical of the income, namely Jeffreys prior and the corresponding Fisher information: the fact that the density involves an indicator function and is thus discontinuous in the location μ at the observation x makes the likelihood function not differentiable and hence the derivation of the Fisher information not strictly valid. Since the indicator part cannot be differentiated. Not that I am seeing the Jeffreys prior as the ultimate grail for non-informative priors, far from it, but there is definitely something specific in the discontinuity in the density. (In connection with the later point, Weiss and Suchard deliver a highly critical commentary on the non-need for reference priors and the preference given to a non-parametric Bayes primary analysis. Maybe making the point towards a greater convergence of the two perspectives, objective Bayes and non-parametric Bayes.)

This paper and the ensuing discussion about the properness of the Jeffreys posterior reminded me of our earliest paper on the topic with Jean Diebolt. Where we used improper priors on location and scale parameters but prohibited allocations (in the Gibbs sampler) that would lead to less than two observations per components, thereby ensuring that the (truncated) posterior was well-defined. (This feature also remained in the Series B paper, submitted at the same time, namely mid-1990, but only published in 1994!)  Larry Wasserman proved ten years later that this truncation led to consistent estimators, but I had not thought about it in very long while. I still like this notion of forcing some (enough) datapoints into each component for an allocation (of the latent indicator variables) to be an acceptable Gibbs move. This is obviously not compatible with the iid representation of a mixture model, but it expresses the requirement that components all have a meaning in terms of the data, namely that all components contributed to generating a part of the data. This translates as a form of weak prior information on how much we trust the model and how meaningful each component is (in opposition to adding meaningless extra-components with almost zero weights or almost identical parameters).

As a marginalia, the insistence in Rubio and Steel’s paper that all observations in the sample be different also reminded me of a discussion I wrote for one of the Valencia proceedings (Valencia 6 in 1998) where Mark presented a paper with Carmen Fernández on this issue of handling duplicated observations modelled by absolutely continuous distributions. (I am afraid my discussion is not worth the $250 price tag given by amazon!)

Bayesian Data Analysis [BDA3 – part #2]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , on March 31, 2014 by xi'an

Here is the second part of my review of Gelman et al.’ Bayesian Data Analysis (third edition):

“When an iterative simulation algorithm is “tuned” (…) the iterations will not in general converge to the target distribution.” (p.297)

Part III covers advanced computation, obviously including MCMC but also model approximations like variational Bayes and expectation propagation (EP), with even a few words on ABC. The novelties in this part are centred at Stan, the language Andrew is developing around Hamiltonian Monte Carlo techniques, a sort of BUGS of the 10’s! (And of course Hamiltonian Monte Carlo techniques themselves. A few (nit)pickings: the book advises important resampling without replacement (p.266) which makes some sense when using a poor importance function but ruins the fundamentals of importance sampling. Plus, no trace of infinite variance importance sampling? of harmonic means and their dangers? In the Metropolis-Hastings algorithm, the proposal is called the jumping rule and denoted by Jt, which, besides giving the impression of a Jacobian, seems to allow for time-varying proposals and hence time-inhomogeneous Markov chains, which convergence properties are much hairier. (The warning comes much later, as exemplified in the above quote.) Moving from “burn-in” to “warm-up” to describe the beginning of an MCMC simulation. Being somewhat 90’s about convergence diagnoses (as shown by the references in Section 11.7), although the book also proposes new diagnoses and relies much more on effective sample sizes. Particle filters are evacuated in hardly half-a-page. Maybe because Stan does not handle particle filters. A lack of intuition about the Hamiltonian Monte Carlo algorithms, as the book plunges immediately into a two-page pseudo-code description. Still using physics vocabulary that put me (and maybe only me) off. Although I appreciated the advice to check analytical gradients against their numerical counterpart.

“In principle there is no limit to the number of levels of variation that can be handled in this way. Bayesian methods provide ready guidance in handling the estimation of the unknown parameters.” (p.381)

I also enjoyed reading the part about modes that stand at the boundary of the parameter space (Section 13.2), even though I do not think modes are great summaries in Bayesian frameworks and while I do not see how picking the prior to avoid modes at the boundary avoids the data impacting the prior, in fine. The variational Bayes section (13.7) is equally enjoyable, with a proper spelled-out illustration, introducing an unusual feature for Bayesian textbooks.  (Except that sampling without replacement is back!) Same comments for the Expectation Propagation (EP) section (13.8) that covers brand new notions. (Will they stand the test of time?!)

“Geometrically, if β-space is thought of as a room, the model implied by classical model selection claims that the true β has certain prior probabilities of being in the room, on the floor, on the walls, in the edge of the room, or in a corner.” (p.368)

Part IV is a series of five chapters about regression(s). This is somewhat of a classic, nonetheless  Chapter 14 surprised me with an elaborate election example that dabbles in advanced topics like causality and counterfactuals. I did not spot any reference to the g-prior or to its intuitive justifications and the chapter mentions the lasso as a regularisation technique, but without any proper definition of this “popular non-Bayesian form of regularisation” (p.368). In French: with not a single equation! Additional novelty may lie in the numerical prior information about the correlations. What is rather crucially (cruelly?) missing though is a clearer processing of variable selection in regression models. I know Andrew opposes any notion of a coefficient being exactly equal to zero, as ridiculed through the above quote, but the book does not reject model selection, so why not in this context?! Chapter 15 on hierarchical extensions stresses the link with exchangeability, once again. With another neat election example justifying the progressive complexification of the model and the cranks and toggles of model building. (I am not certain the reparameterisation advice on p.394 is easily ingested by a newcomer.) The chapters on robustness (Chap. 17) and missing data (Chap. 18) sound slightly less convincing to me, esp. the one about robustness as I never got how to make robustness agree with my Bayesian perspective. The book states “we do not have to abandon Bayesian principles to handle outliers” (p.436), but I would object that the Bayesian paradigm compels us to define an alternative model for those outliers and the way they are produced. One can always resort to a drudging exploration of which subsample of the dataset is at odds with the model but this may be unrealistic for large datasets and further tells us nothing about how to handle those datapoints. The missing data chapter is certainly relevant to such a comprehensive textbook and I liked the survey illustration where the missing data was in fact made of missing questions. However, I felt the multiple imputation part was not well-presented, fearing readers would not understand how to handle it…

“You can use MCMC, normal approximation, variational Bayes, expectation propagation, Stan, or any other method. But your fit must be Bayesian.” (p.517)

Part V concentrates the most advanced material, with Chapter 19 being mostly an illustration of a few complex models, slightly superfluous in my opinion, Chapter 20 a very short introduction to functional bases, including a basis selection section (20.2) that implements the “zero coefficient” variable selection principle refuted in the regression chapter(s), and does not go beyond splines (what about wavelets?), Chapter 21 a (quick) coverage of Gaussian processes with the motivating birth-date example (and two mixture datasets I used eons ago…), Chapter 22 a more (too much?) detailed study of finite mixture models, with no coverage of reversible-jump MCMC, and Chapter 23 an entry on Bayesian non-parametrics through Dirichlet processes.

“In practice, for well separated components, it is common to remain stuck in one labelling across all the samples that are collected. One could argue that the Gibbs sampler has failed in such a case.” (p.535)

To get back to mixtures, I liked the quote about the label switching issue above, as I was “one” who argued that the Gibbs sampler fails to converge! The corresponding section seems to favour providing a density estimate for mixture models, rather than component-wise evaluations, but it nonetheless mentions the relabelling by permutation approach (if missing our 2000 JASA paper). The section about inferring on the unknown number of components suggests conducting a regular Gibbs sampler on a model with an upper bound on the number of components and then checking for empty components, an idea I (briefly) considered in the mid-1990’s before the occurrence of RJMCMC. Of course, the prior on the components matters and the book suggests using a Dirichlet with fixed sum like 1 on the coefficients for all numbers of components.

“14. Objectivity and subjectivity: discuss the statement `People tend to believe results that support their preconceptions and disbelieve results that surprise them. Bayesian methods tend to encourage this undisciplined mode of thinking.’¨ (p.100)

Obviously, this being a third edition begets the question, what’s up, doc?!, i.e., what’s new [when compared with the second edition]? Quite a lot, even though I am not enough of a Gelmanian exegist to produce a comparision table. Well, for a starter, David Dunson and Aki Vethtari joined the authorship, mostly contributing to the advanced section on non-parametrics, Gaussian processes, EP algorithms. Then the Hamiltonian Monte Carlo methodology and Stan of course, which is now central to Andrew’s interests. The book does include a short Appendix on running computations in R and in Stan. Further novelties were mentioned above, like the vision of weakly informative priors taking over noninformative priors but I think this edition of Bayesian Data Analysis puts more stress on clever and critical model construction and on the fact that it can be done in a Bayesian manner. Hence the insistence on predictive and cross-validation tools. The book may be deemed somewhat short on exercices, providing between 3 and 20 mostly well-developed problems per chapter, often associated with datasets, rather than the less exciting counter-example above. Even though Andrew disagrees and his students at ENSAE this year certainly did not complain, I personally feel a total of 220 exercices is not enough for instructors and self-study readers. (At least, this reduces the number of email requests for solutions! Esp. when 50 of those are solved on the book website.) But this aspect is a minor quip: overall this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis.

Bayesian Data Analysis [BDA3]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , on March 28, 2014 by xi'an

Andrew Gelman and his coauthors, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin, have now published the latest edition of their book Bayesian Data Analysis. David and Aki are newcomers to the authors’ list, with an extended section on non-linear and non-parametric models. I have been asked by Sam Behseta to write a review of this new edition for JASA (since Sam is now the JASA book review editor). After wondering about my ability to produce an objective review (on the one hand, this is The Competition  to Bayesian Essentials!, on the other hand Andrew is a good friend spending the year with me in Paris), I decided to jump for it and write a most subjective review, with the help of Clara Grazian who was Andrew’s teaching assistant this year in Paris and maybe some of my Master students who took Andrew’s course. The second edition was reviewed in the September 2004 issue of JASA and we now stand ten years later with an even more impressive textbook. Which truly what Bayesian data analysis should be.

This edition has five parts, Fundamentals of Bayesian Inference, Fundamentals of Bayesian Data Analysis, Advanced Computation, Regression Models, and Non-linear and Non-parametric Models, plus three appendices. For a total of xiv+662 pages. And a weight of 2.9 pounds (1395g on my kitchen scale!) that makes it hard to carry around in the metro…. I took it to Warwick (and then Nottingham and Oxford and back to Paris) instead.

We could avoid the mathematical effort of checking the integrability of the posterior density (…) The result would clearly show the posterior contour drifting off toward infinity.” (p.111)

While I cannot go into a detailed reading of those 662 pages (!), I want to highlight a few gems. (I already wrote a detailed and critical analysis of Chapter 6 on model checking in that post.) The very first chapter provides all the necessary items for understanding Bayesian Data Analysis without getting bogged in propaganda or pseudo-philosophy. Then the other chapters of the first part unroll in a smooth way, cruising on the B highway… With the unique feature of introducing weakly informative priors (Sections 2.9 and 5.7), like the half-Cauchy distribution on scale parameters. It may not be completely clear how weak a weakly informative prior, but this novel notion is worth including in a textbook. Maybe a mild reproach at this stage: Chapter 5 on hierarchical models is too verbose for my taste, as it essentially focus on the hierarchical linear model. Of course, this is an essential chapter as it links exchangeability, the “atom” of Bayesian reasoning used by de Finetti, with hierarchical models. Still. Another comment on that chapter: it broaches on the topic of improper posteriors by suggesting to run a Markov chain that can exhibit improperness by enjoying an improper behaviour. When it happens as in the quote above, fine!, but there is no guarantee this is always the case! For instance, improperness may be due to regions near zero rather than infinity. And a last barb: there is a dense table (Table 5.4, p.124) that seems to run contrariwise to Andrew’s avowed dislike of tables. I could also object at the idea of a “true prior distribution” (p.128), or comment on the trivia that hierarchical chapters seem to attract rats (as I also included a rat example in the hierarchical Bayes chapter of Bayesian Choice and so does the BUGS Book! Hence, a conclusion that Bayesian textbooks are better be avoided by muriphobiacs…)

“Bayes factors do not work well for models that are inherently continuous (…) Because we emphasize continuous families of models rather than discrete choices, Bayes factors are rarely relevant in our approach to Bayesian statistics.” (p.183 & p.193)

Part II is about “the creative choices that are required, first to set up a Bayesian model in a complex problem, then to perform the model checking and confidence building that is typically necessary to make posterior inferences scientifically defensible” (p.139). It is certainly one of the strengths of the book that it allows for a critical look at models and tools that are rarely discussed in more theoretical Bayesian books. As detailed in my  earlier post on Chapter 6, model checking is strongly advocated, via posterior predictive checks and… posterior predictive p-values, which are at best empirical indicators that something could be wrong, definitely not that everything’s allright! Chapter 7 is the model comparison equivalent of Chapter 6, starting with the predictive density (aka the evidence or the marginal likelihood), but completely bypassing the Bayes factor for information criteria like the Watanabe-Akaike or widely available information criterion (WAIC), and advocating cross-validation, which is empirically satisfying but formally hard to integrate within a full Bayesian perspective. Chapter 8 is about data collection, sample surveys, randomization and related topics, another entry that is missing from most Bayesian textbooks, maybe not that surprising given the research topics of some of the authors. And Chapter 9 is the symmetric in that it focus on the post-modelling step of decision making.

(Second part of the review to appear on Monday, leaving readers the weekend to recover!)

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