Archive for Mexico

a ghastly data visualisation

Posted in Books, pictures, Statistics with tags , , , , , , on August 15, 2021 by xi'an

stratified MCMC

Posted in Books, pictures, Statistics with tags , , , , , , , , , , , , on December 3, 2020 by xi'an

When working last week with a student, we came across [the slides of a talk at ICERM by Brian van Koten about] a stratified MCMC method whose core idea is to solve a eigenvector equation z’=z’F associated with the masses of “partition” functions Ψ evaluated at the target. (The arXived paper is also available since 2017 but I did not check it in more details.)Although the “partition” functions need to overlap for the matrix not to be diagonal (actually the only case that does not work is when these functions are truly indicator functions). As in other forms of stratified sampling, the practical difficulty is in picking the functions Ψ so that the evaluation of the terms of the matrix F is not overly impacted by the Monte Carlo error. If spending too much time in estimating these terms, there is not a clear gain in switching to stratified sampling, which may be why it is not particularly developed in the MCMC literature….

As an interesting aside, the illustration in this talk comes from the Mexican stamp thickness data I also used in my earlier mixture papers, concerning the 1872 Hidalgo issue that was printed on different qualities of paper. This makes the number k of components somewhat uncertain, although k=3 is sometimes used as a default. Hence a parameter and simulation space of dimension 8, even though the method is used toward approximating the marginal posteriors on the weights λ¹ and λ².

post-COVID post-conference mood

Posted in Kids, Running, Travel, University life with tags , , , , , , , , , , , , , on August 27, 2020 by xi'an

Nature ran a 4-page comment on the post-COVID future of massive conferences (NeurIPS or JSM style) and on how to make them less carbon greedy. Some of their common-sense suggestions come close to what I had suggested a while ago and some became promptly implemented in these times of COVID-19 travel restrictions, as, e.g., to systematically include virtual attendance option(s), with provisions from one’s institutions for quality time (as if one was indeed away), to add multiple (3?) regional hubs to a single location, which also offers the perk of a round-the-clock meeting, with an optimisation of the three places chosen to minimise (estimated) total flight distances for the potential participants, as in e.g. choosing U.S. central Chicago rather than extremes like Seattle or Miami, and possibly adding Tokyo and Paris, to reduce the frequency of the monster meetings by coordinating with sister societies, to enforce an individual or institutional maximum yearly budget, to have corporate sponsors turning from travel support to improving remote access in less favoured countries.

Obviously, it seems difficult to completely switch to a fully virtual solution, as attending a conference has many academic dimensions to be accounted for, but the “big ones” should be the first to shrink, if only because the most impacting. And also because small, high quality workshops have much more impact research-wise on their attendants. With the above still offering some savings. And also the possibility to bypass financial, personal, visa, political, life-threatening impossibilities to attend a meeting in a specific foreign country. Provided uncensored remote communication tools are allowed or possible from the said  country. (Calling for the question, barring financial difficulties, and once COVID-related restrictions have been lifted, what are the countries where everyone could consider attending?!)

This year, before lockdown forced the cancellation of ABC in Grenoble, we had set a mirror version in Warwick. Which led us to create the One World ABC seminar. The Bernoulli-IMS World congress was postponed by one year but a few dedicated volunteers managed to build within a few weeks a free impressive virtual substitute with more than 600 talks and close to 2000 participants (so far). Remember it is to take place on 24-28 August, on different time zones and with ten live plenaries repeated twice to this effect.

Next year, we still hope to organise an Objective Bayesian workshop at Casa Matemática Oaxaca (CMO) in México and the current sanitary conditions imply a reduction of the physically present participants by two thirds. Meaning for certain a remote component and possibly a mirror location depending on the state of the World in December 2021.

a memory called Empire [book review]

Posted in Books, Kids, pictures, Travel with tags , , , , , , , , , , , on June 6, 2020 by xi'an

A pleasant read for a few afternoon breaks (and vitamin D intake), that I chose as it was nominated for the Hugo and Nebula awards as well as a Not the Booker Prize Guardian choice. But not really worth the hype I think as the novel, A Memory Called Empire, is quite unidimensional (which is unfortunate for a space opera). In that the few characters that populate the book manage to move by themselves the political structure of the interstellar universe quite substantially. Within a few days. These characters are definitely attractive but somewhat too nice to be true and the way they bond and connect with one another is just implausible, even for a science fiction novel

“…no algorithm is innocent of its designersAn algorithm is only as perfect as the person designing it.”

The most interesting part in the story, although somewhat stretched too thin, is the conflict the central character feels between her attraction to the highly sophisticated culture of the Empire and the feeling that she will never be fully incorporated within that culture. Despite mastering the language and the societal codes well-enough to reach the upper spheres of society and impact them.

“…the real inspiration for the number-noun naming system comes from the naming practices of the Mixtec people of Oaxaca…” Arkady Martine

But, beside borrowing a lot to Japanese culture, and a wee bit to Maya or Aztec societies, the universe created by Arkady Martine is quite close to ours in its mundane aspects, including plastic spoons..! With very few truly novel technologies. But with email delivered on USB keys after travelling faster than light between star systems. The threat of an alien invasion is pending, by the end of the book, paving the way for an incoming second volume.To be read…

focused Bayesian prediction

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on June 3, 2020 by xi'an

In this fourth session of our One World ABC Seminar, my friend and coauthor Gael Martin, gave an after-dinner talk on focused Bayesian prediction, more in the spirit of Bissiri et al. than following a traditional ABC approach.  because along with Ruben Loaiza-Maya and [my friend and coauthor] David Frazier, they consider the possibility of a (mild?) misspecification of the model. Using thus scoring rules à la Gneiting and Raftery. Gael had in fact presented an earlier version at our workshop in Oaxaca, in November 2018. As in other solutions of that kind, difficulty in weighting the score into a distribution. Although asymptotic irrelevance, direct impact on the current predictions, at least for the early dates in the time series… Further calibration of the set of interest A. Or the focus of the prediction. As a side note the talk perfectly fits the One World likelihood-free seminar as it does not use the likelihood function!

“The very premise of this paper is that, in reality, any choice of predictive class is such that the truth is not contained therein, at which point there is no reason to presume that the expectation of any particular scoring rule will be maximized at the truth or, indeed, maximized by the same predictive distribution that maximizes a different (expected) score.”

This approach requires the proxy class to be close enough to the true data generating model. Or in the word of the authors to be plausible predictive models. And to produce the true distribution via the score as it is proper. Or the closest to the true model in the misspecified family. I thus wonder at a possible extension with a non-parametric version, the prior being thus on functionals rather than parameters, if I understand properly the meaning of Π(Pθ). (Could the score function be misspecified itself?!) Since the score is replaced with its empirical version, the implementation is  resorting to off-the-shelf MCMC. (I wonder for a few seconds if the approach could be seen as a pseudo-marginal MCMC but the estimation is always based on the same observed sample, hence does not directly fit the pseudo-marginal MCMC framework.)

[Notice: Next talk in the series is tomorrow, 11:30am GMT+1.]

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