Archive for Dirichlet mixture priors

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):