## latent nested nonparametric priors

Posted in Books, Statistics with tags , , , , , , , on September 23, 2019 by xi'an

A paper on an extended type of non-parametric priors by Camerlenghi et al. [all good friends!] is about to appear in Bayesian Analysis, with a discussion open for contributions (until October 15). While a fairly theoretical piece of work, it validates a Bayesian approach for non-parametric clustering of separate populations with, broadly speaking, common clusters. More formally, it constructs a new family of models that allows for a partial or complete equality between two probability measures, but does not force full identity when the associated samples do share some common observations. Indeed, the more traditional structures prohibit one or the other, from the Dirichlet process (DP) prohibiting two probability measure realisations from being equal or partly equal to some hierarchical DP (HDP) already allowing for common atoms across measure realisations, but prohibiting complete identity between two realised distributions, to nested DP offering one extra level of randomness, but with an infinity of DP realisations that prohibits common atomic support besides completely identical support (and hence distribution).

The current paper imagines two realisations of random measures written as a sum of a common random measure and of one of two separate almost independent random measures: (14) is the core formula of the paper that allows for partial or total equality. An extension to a setting larger than facing two samples seems complicated if only because of the number of common measures one has to introduce, from the totally common measure to measures that are only shared by a subset of the samples. Except in the simplified framework when a single and universally common measure is adopted (with enough justification). The randomness of the model is handled via different completely random measures that involved something like four degrees of hierarchy in the Bayesian model.

Since the example is somewhat central to the paper, the case of one or rather two two-component Normal mixtures with a common component (but with different mixture weights) is handled by the approach, although it seems that it was already covered by HDP. Having exactly the same term (i.e., with the very same weight) is not, but this may be less interesting in real life applications. Note that alternative & easily constructed & parametric constructs are already available in this specific case, involving a limited prior input and a lighter computational burden, although the  Gibbs sampler behind the model proves extremely simple on the paper. (One may wonder at the robustness of the sampler once the case of identical distributions is visited.)

Due to the combinatoric explosion associated with a higher number of observed samples, despite obvious practical situations,  one may wonder at any feasible (and possibly sequential) extension, that would further keep a coherence under marginalisation (in the number of samples). And also whether or not multiple testing could be coherently envisioned in this setting, for instance when handling all hospitals in the UK. Another consistency question covers the Bayes factor used to assess whether the two distributions behind the samples are or not identical. (One may wonder at the importance of the question, hopefully applied to more relevant dataset than the Iris data!)

## parallelizable sampling method for parameter inference of large biochemical reaction models

Posted in Books, Statistics with tags , , , , , , , , on June 18, 2018 by xi'an

I came across this older (2016) arXiv paper by Jan Mikelson and Mustafa Khammash [antidated as of April 25, 2018] as another version of nested sampling. The novelty of the approach is in applying nested sampling for approximating the likelihood function in the case of involved hidden Markov models (although the name itself does not appear in the paper). This is an interesting proposal, even though there is a fairly large and very active literature on computational approaches to such objects, from sequential Monte Carlo (SMC) to particle MCMC (pMCMC), to SMC².

“We found a way to efficiently sample parameter vectors (particles) from the super level set of the likelihood (sets of particles with a likelihood equal to or higher than some threshold) corresponding to an increasing sequence of thresholds” (p.2)

The approach here is an aggregate of nested sampling and particle filters (SMC), filters that are paradoxically employed in approximating the likelihood function itself, thus called repeatedly as the value of the parameter θ changes, unless I am confused, when it seems to me that, once started with particle filters, the authors could have used them all the way to the upper level (through, again, SMC²). Instead, and that brings a further degree of (uncorrected) approximation to the procedure, a Dirichlet process prior is used to estimate Gaussian mixture approximations to the true posterior distribution(s) on the (super) level sets. Now, approximating a distribution that is zero outside a compact set [the prior restricted to the likelihood being larger than by a distribution with an infinite support does not a priori sound like a particularly enticing idea. Note also that there is no later correction for using the mixture approximation to the restricted prior. (The method also involves an approximation of the (Lebesgue) volume of the level sets that may be poor in higher dimensions.)

“DP-GMM estimations work very well in high dimensional spaces and since we use rejection sampling to obtain samples from the level set by sampling from the DP-GMM estimation, the estimation error does not get propagated through iterations.” (p.13)

One aspect of the paper that puzzles me is the use of a rejection sampler to produce new parameters simulations from a given (super) level set, as this involves a lower bound M on the Gaussian mixture approximation over this level set. If a Gaussian mixture approximation is available, there is apparently no need for this as it can be sampled directly and values below the threshold can be disposed of. It is also unclear why the error does not propagate from one level to the next, if only because of the connection between the successive particle approximations.

## a Ca’Foscari [first Italian-French statistics seminar]

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on October 26, 2017 by xi'an

Apart from subjecting my [surprisingly large!] audience to three hours of ABC tutorial today, and after running Ponte della la Libertà to Mestre and back in a deep fog, I attended the second part of the 1st Italian-French statistics seminar at Ca’Foscari, Venetiarum Universitas, with talks by Stéfano Tonellato and Roberto Casarin. Stéfano discussed a most interesting if puzzling notion of clustering via Dirichlet process mixtures. Which indeed puzzles me for its dependence on the Dirichlet measure and on the potential for an unlimited number of clusters as the sample size increases. The method offers similarities with an approach from our 2000 JASA paper on running inference on mixtures without proper label switching, in that looking at pairs of allocated observations to clusters is revealing about the [true or pseudo-true] number of clusters. With divergence in using eigenvalues of Laplacians on similarity matrices. But because of the potential for the number of components to diverge I wonder at the robustness of the approach via non-parametric [Bayesian] modelling. Maybe my difficulty stands with the very notion of cluster, which I find poorly defined and mostly in the eyes of the beholder! And Roberto presented a recent work on SURE and VAR models, with a great graphical representation of the estimated connections between factors in a sparse graphical model.

## repulsive mixtures

Posted in Books, Statistics with tags , , , , , , , , on April 10, 2017 by xi'an

Fangzheng Xie and Yanxun Xu arXived today a paper on Bayesian repulsive modelling for mixtures. Not that Bayesian modelling is repulsive in any psychological sense, but rather that the components of the mixture are repulsive one against another. The device towards this repulsiveness is to add a penalty term to the original prior such that close means are penalised. (In the spirit of the sugar loaf with water drops represented on the cover of Bayesian Choice that we used in our pinball sampler, repulsiveness being there on the particles of a simulated sample and not on components.) Which means a prior assumption that close covariance matrices are of lesser importance. An interrogation I have has is was why empty components are not excluded as well, but this does not make too much sense in the Dirichlet process formulation of the current paper. And in the finite mixture version the Dirichlet prior on the weights has coefficients less than one.

The paper establishes consistency results for such repulsive priors, both for estimating the distribution itself and the number of components, K, under a collection of assumptions on the distribution, prior, and repulsiveness factors. While I have no mathematical issue with such results, I always wonder at their relevance for a given finite sample from a finite mixture in that they give an impression that the number of components is a perfectly estimable quantity, which it is not (in my opinion!) because of the fluid nature of mixture components and therefore the inevitable impact of prior modelling. (As Larry Wasserman would pound in, mixtures like tequila are evil and should likewise be avoided!)

The implementation of this modelling goes through a “block-collapsed” Gibbs sampler that exploits the latent variable representation (as in our early mixture paper with Jean Diebolt). Which includes the Old Faithful data as an illustration (for which a submission of ours was recently rejected for using too old datasets). And use the logarithm of the conditional predictive ordinate as  an assessment tool, which is a posterior predictive estimated by MCMC, using the data a second time for the fit.

## Monte Carlo in the convent

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , on July 14, 2016 by xi'an

Last week, at the same time as the workshop on retrospective Monte Carlo in Warwick, there was a Monte Carlo conference in Paris, closing a Monte Carlo cycle run by Institut Louis Bachelier from October 2015 till June 2016. It took place in the convent of Les Cordeliers, downtown Paris [hence the title] and I alas could not attend the talks. As I organised a session on Bayesian (approximate) computations, with Richard Everitt, Jere Koskela, and Chris Sherlock as speakers (and Robin Ryder as chair), here are the slides of the speakers (actually, Jere most kindly agreed to give Chris’ talk as Chris was to sick to travel to Paris):

## mixture models with a prior on the number of components

Posted in Books, Statistics, University life with tags , , , , , , , on March 6, 2015 by xi'an

“From a Bayesian perspective, perhaps the most natural approach is to treat the numberof components like any other unknown parameter and put a prior on it.”

Another mixture paper on arXiv! Indeed, Jeffrey Miller and Matthew Harrison recently arXived a paper on estimating the number of components in a mixture model, comparing the parametric with the non-parametric Dirichlet prior approaches. Since priors can be chosen towards agreement between those. This is an obviously interesting issue, as they are often opposed in modelling debates. The above graph shows a crystal clear agreement between finite component mixture modelling and Dirichlet process modelling. The same happens for classification.  However, Dirichlet process priors do not return an estimate of the number of components, which may be considered a drawback if one considers this is an identifiable quantity in a mixture model… But the paper stresses that the number of estimated clusters under the Dirichlet process modelling tends to be larger than the number of components in the finite case. Hence that the Dirichlet process mixture modelling is not consistent in that respect, producing parasite extra clusters…

In the parametric modelling, the authors assume the same scale is used in all Dirichlet priors, that is, for all values of k, the number of components. Which means an incoherence when marginalising from k to (k-p) components. Mild incoherence, in fact, as the parameters of the different models do not have to share the same priors. And, as shown by Proposition 3.3 in the paper, this does not prevent coherence in the marginal distribution of the latent variables. The authors also draw a comparison between the distribution of the partition in the finite mixture case and the Chinese restaurant process associated with the partition in the infinite case. A further analogy is that the finite case allows for a stick breaking representation. A noteworthy difference between both modellings is about the size of the partitions

$\mathbb{P}(s_1,\ldots,s_k)\propto\prod_{j=1}^k s_j^{-\gamma}\quad\text{versus}\quad\mathbb{P}(s_1,\ldots,s_k)\propto\prod_{j=1}^k s_j^{-1}$

in the finite (homogeneous partitions) and infinite (extreme partitions) cases.

An interesting entry into the connections between “regular” mixture modelling and Dirichlet mixture models. Maybe not ultimately surprising given the past studies by Peter Green and Sylvia Richardson of both approaches (1997 in Series B and 2001 in JASA).

## Bayesian non-parametrics

Posted in Statistics with tags , , , , , , , , , , , on April 8, 2013 by xi'an

Here is a short discussion I wrote yesterday with Judith Rousseau of a paper by Peter Müller and Riten Mitra to appear in Bayesian Analysis.

“We congratulate the authors for this very pleasant overview of the type of problems that are currently tackled by Bayesian nonparametric inference and for demonstrating how prolific this field has become. We do share the authors viewpoint that many Bayesian nonparametric models allow for more flexible modelling than parametric models and thus capture finer details of the data. BNP can be a good alternative to complex parametric models in the sense that the computations are not necessarily more difficult in Bayesian nonparametric models. However we would like to mitigate the enthusiasm of the authors since, although we believe that Bayesian nonparametric has proved extremely useful and interesting, we think they oversell the “nonparametric side of the Force”! Our main point is that by definition, Bayesian nonparametric is based on prior probabilities that live on infinite dimensional spaces and thus are never completely swamped by the data. It is therefore crucial to understand which (or why!) aspects of the model are strongly influenced by the prior and how.

As an illustration, when looking at Example 1 with the censored zeroth cell, our reaction is that this is a problem with no proper solution, because it is lacking too much information. In other words, unless some parametric structure of the model is known, in which case the zeroth cell is related with the other cells, we see no way to infer about the size of this cell. The outcome produced by the authors is therefore unconvincing to us in that it seems to only reflect upon the prior modelling (α,G*) and not upon the information contained in the data. Now, this prior modelling may be to some extent justified based on side information about the medical phenomenon under study, however its impact on the resulting inference is palatable.

Recently (and even less recently) a few theoretical results have pointed out this very issue. E.g., Diaconis and Freedman (1986) showed that some priors could surprisingly lead to inconsistent posteriors, even though it was later shown that many priors lead to consistent posteriors and often even to optimal asymptotic frequentist estimators, see for instance van der Vaart and van Zanten (2009) and Kruijer et al. (2010). The worry about Bayesian nonparametrics truly appeared when considering (1) asymptotic frequentist properties of semi-parametric procedures; and (2) interpretation of inferential aspects of Bayesian nonparametric procedures. It was shown in various instances that some nonparametric priors which behaved very nicely for the estimation of the whole parameter could have disturbingly suboptimal behaviour for some specific functionals of interest, see for instance Arbel et al. (2013) and Rivoirard and Rousseau (2012). We do not claim here that asymptotics is the answer to everything however bad asymptotic behaviour shows that something wrong is going on and this helps understanding the impact of the prior. These disturbing bad results are an illustration that in these infinite dimensional models the impact of the prior modelling is difficult to evaluate and that although the prior looks very flexible it can in fact be highly informative and/or restrictive for some aspects of the parameter. It would thus be wrong to conclude that every aspect of the parameter is well-recovered because some are. It has been a well-known fact for Bayesian parametric models, leading to extensive research on reference and other types of objective priors. It is even more crucial in the nonparametric world. No (nonparametric) prior can be suited for every inferential aspect and it is important to understand which aspects of the parameter are well-recovered and which ones are not.

We also concur with the authors that Dirichlet mixture priors provide natural clustering mechanisms, but one may question the “natural” label as the resulting clustering is quite unstructured, growing in the number of clusters as the number of observations increases and not incorporating any prior constraint on the “definition” of a cluster, except the one implicit and well-hidden behind the non-parametric prior. In short, it is delicate to assess what is eventually estimated by this clustering methods.

These remarks are not to be taken criticisms of the overall Bayesian nonparametric approach, just the contrary. We simply emphasize (or recall) that there is no such thing as a free lunch and that we need to post the price to pay for potential customers. In these models, this is far from easy and just as far from being completed.”

References

• Arbel, J., Gayraud, G., and Rousseau, J. (2013). Bayesian adaptive optimal estimation using a sieve prior. Scandinavian Journal of Statistics, to appear.

• Diaconis, P. and Freedman, D. (1986). On the consistency of Bayes estimates. Ann. Statist., 14:1-26.

• Kruijer, W., Rousseau, J., and van der Vaart, A. (2010). Adaptive Bayesian density estimation with location-scale mixtures. Electron. J. Stat., 4:1225-1257.

• Rivoirard, V. and Rousseau, J. (2012). On the Bernstein Von Mises theorem for linear functionals of the density. Ann. Statist., 40:1489-1523.

• van der Vaart, A. and van Zanten, J. H. (2009). Adaptive Bayesian estimation using a Gaussian random field with inverse Gamma bandwidth. Ann. Statist., 37:2655-2675.