Archive for Statistics and Computing

MCMC, with common misunderstandings

Posted in Books, pictures, R, Statistics, University life with tags , , , , , , , , , , , , on January 27, 2020 by xi'an

As I was asked to write a chapter on MCMC methods for an incoming Handbook of Computational Statistics and Data Science, published by Wiley, rather than cautiously declining!, I decided to recycle the answers I wrote on X validated to what I considered to be the most characteristic misunderstandings about MCMC and other computing methods, using as background the introduction produced by Wu Changye in his PhD thesis. Waiting for the opinion of the editors of the Handbook on this Q&A style. The outcome is certainly lighter than other recent surveys like the one we wrote with Peter Green, Krys Latuszinski, and Marcelo Pereyra, for Statistics and Computing, or the one with Victor Elvira, Nick Tawn, and Changye Wu.

Springer no more!

Posted in Books, Kids, Statistics, University life with tags , , , , , on April 4, 2018 by xi'an

Just learned that, starting from tomorrow night, I will not have access to any of the Springer journals, as the negotiations between the consortium of French universities, research institutes, higher educations schools, and museums, failed. The commercial published refusing to stem the ever increasing fees, while happily taking in the fast increasing open access fees it pressures from authors, a unique example of triple taxation (researchers’ salaries, open access duties, and enormous non-negotiable subscription rates for the whole package of journals)… Following their German counterparts. Well, this is an opportunity for the boards of all these journals to withdraw and create the phantom version of their formal journal, evaluating and reviewing papers already available on arXiv! And I should definitely get my acts together, rise from my winter-is-coming lethargy, and launch PCI Comput Stat now!!!

parameter space for mixture models

Posted in Statistics, University life with tags , , , on March 24, 2017 by xi'an

“The paper defines a new solution to the problem of defining a suitable parameter space for mixture models.”

When I received the table of contents of the incoming Statistics & Computing and saw a paper by V. Maroufy and P. Marriott about the above, I was quite excited about a new approach to mixture parameterisation. Especially after our recent reposting of the weakly informative reparameterisation paper. Alas, after reading the paper, I fail to see the (statistical) point of the whole exercise.

Starting from the basic fact that mixtures face many identifiability issues, not only invariance by component permutation, but the possibility to add spurious components as well, the authors move to an entirely different galaxy by defining mixtures of so-called local mixtures. Developed by one of the authors. The notion is just incomprehensible for me: the object is a weighted sum of the basic component of the original mixture, e.g., a Normal density, and of k of its derivatives wrt its mean, a sort of parameterised Taylor expansion. Which implies the parameter is unidimensional, incidentally. The weights of this strange mixture are furthermore constrained by the positivity of the resulting mixture, a constraint that seems impossible to satisfy in the Normal case when the number of derivatives is odd. And hard to analyse in any case since possibly negative components do not enjoy an interpretation as a probability density. In exponential families, the local mixture is the original exponential family density multiplied by a polynomial. The current paper moves one step further [from the reasonable] by considering mixtures [in the standard sense] of such objects. Which components are parameterised by their mean parameter and a collection of weights. The authors then restrict the mean parameters to belong to a finite and fixed set, which elements are coerced by a maximum error rate on any compound distribution derived from this exponential family structure. The remainder of the paper discusses of the choice of the mean parameters and of an EM algorithm to estimate the parameters, with a confusing lower bound on the mixture weights that impacts the estimation of the weights. And no mention made of the positivity constraint. I remain completely bemused by the paper and its purpose: I do not even fathom how this qualifies as a mixture.

Statistics & Computing [toc]

Posted in Books, Statistics with tags , , , , on June 29, 2016 by xi'an

6The latest [June] issue of Statistics & Computing is full of interesting Bayesian and Monte Carlo entries, some of which are even open access!

 

more of the same!

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on December 10, 2015 by xi'an

aboriginal artist, NGV, Melbourne, July 30, 2012Daniel Seita, Haoyu Chen, and John Canny arXived last week a paper entitled “Fast parallel SAME Gibbs sampling on general discrete Bayesian networks“.  The distributions of the observables are defined by full conditional probability tables on the nodes of a graphical model. The distributions on the latent or missing nodes of the network are multinomial, with Dirichlet priors. To derive the MAP in such models, although this goal is not explicitly stated in the paper till the second page, the authors refer to the recent paper by Zhao et al. (2015), discussed on the ‘Og just as recently, which applies our SAME methodology. Since the paper is mostly computational (and submitted to ICLR 2016, which takes place juuust before AISTATS 2016), I do not have much to comment about it. Except to notice that the authors mention our paper as “Technical report, Statistics and Computing, 2002”. I am not sure the editor of Statistics and Computing will appreciate! The proper reference is in Statistics and Computing, 12:77-84, 2002.

“We argue that SAME is beneficial for Gibbs sampling because it helps to reduce excess variance.”

Still, I am a wee bit surprised at both the above statement and at the comparison with a JAGS implementation. Because SAME augments the number of latent vectors as the number of iterations increases, so should be slower by a mere curse of dimension,, slower than a regular Gibbs with a single latent vector. And because I do not get either the connection with JAGS: SAME could be programmed in JAGS, couldn’t it? If the authors means a regular Gibbs sampler with no latent vector augmentation, the comparison makes little sense as one algorithm aims at the MAP (with a modest five replicas), while the other encompasses the complete posterior distribution. But this sounds unlikely when considering that the larger the number m of replicas the better their alternative to JAGS. It would thus be interesting to understand what the authors mean by JAGS in this setup!