Archive for ICMS

ICMS supports mirrors

Posted in Travel, University life with tags , , , , on December 6, 2021 by xi'an

Received an announcement from the International Centre for Mathematical Sciences (ICMS) in Edinburgh that they will support mirror meetings (albeit in the United Kindgom) as well as other inclusive initiatives:

• ICMS@: this programme allows organisers to hold ‘satellite events’, ICMS-funded activity at venues elsewhere in the UK with logistic support by the ICMS staff. The aim will be to enable more activities at a high level distributed throughout the country and to facilitate participation by those who cannot easily travel. There will be an additional emphasis on regions that have found it difficult to fund local events in the past.

• Funds to allow participants at ICMS workshops to extend their visits in the UK, especially for the purpose of visiting other institutions and engaging in extended research interaction.

• A visitor programme for researchers from low- and middle-income countries to come to the UK to attend workshops and fund their stay for up to three months.

• Workshops or schools for postgraduate students and early career researchers which can be of varying lengths and intensities.

• A fund to help people with caring responsibilities attend our events.

MCqMC 2022 in Linz, 17-22 July

Posted in Statistics with tags , , , , , , , on August 29, 2020 by xi'an

At the end of MCqMC 2020, held on-line with the amazing support of ICMS in Edinburgh, the next location was announced as being Linz, Austria, hosted by the Johannes Kepler Universität I visited a few years ago (with a memorable run up a nearby hill!). Hopefully this will take place for real as well as on-line, but my prior is rather non-informed at the moment…

limited shelf validity

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , on December 11, 2019 by xi'an

A great article from Steve Stigler in the new, multi-scaled, and so exciting Harvard Data Science Review magisterially operated by Xiao-Li Meng, on the limitations of old datasets. Illustrated by three famous datasets used by three equally famous statisticians, Quetelet, Bortkiewicz, and Gosset. None of whom were fundamentally interested in the data for their own sake. First, Quetelet’s data was (wrongly) reconstructed and missed the opportunity to beat Galton at discovering correlation. Second, Bortkiewicz went looking (or even cherry-picking!) for these rare events in yearly tables of mortality minutely divided between causes such as military horse kicks. The third dataset is not Guinness‘, but a test between two sleeping pills, operated rather crudely over inmates from a psychiatric institution in Kalamazoo, with further mishandling by Gosset himself. Manipulations that turn the data into dead data, as Steve put it. (And illustrates with the above skull collection picture. As well as warning against attempts at resuscitating dead data into what could be called “zombie data”.)

“Successful resurrection is only slightly more common than in Christian theology.”

His global perspective on dead data is that they should stop being used before extending their (shelf) life, rather than turning into benchmarks recycled over and over as a proof of concept. If only (my two cents) because it leads to calibrate (and choose) methods doing well over these benchmarks. Another example that could have been added to the skulls above is the Galaxy Velocity Dataset that makes frequent appearances in works estimating Gaussian mixtures. Which Radford Neal signaled at the 2001 ICMS workshop on mixture estimation as an inappropriate use of the dataset since astrophysical arguments weighted against a mixture modelling.

“…the role of context in shaping data selection and form—context in temporal, political, and social as well as scientific terms—has been shown to be a powerful and interesting phenomenon.”

The potential for “dead-er” data (my neologism!) increases with the epoch in that the careful sleuth work Steve (and others) conducted about these historical datasets is absolutely impossible with the current massive data sets. Massive and proprietary. And presumably discarded once the associated neural net is designed and sold. Letting the burden of unmasking the potential (or highly probable?) biases to others. Most interestingly, this recoups a “comment” in Nature of 17 October by Sabina Leonelli on the transformation of data from a national treasure to a commodity which “ownership can confer and signal power”. But her call for openness and governance of research data seems as illusory as other attempts to sever the GAFAs from their extra-territorial privileges…

label switching by optimal transport: Wasserstein to the rescue

Posted in Books, Statistics, Travel with tags , , , , , , , , , , , , , , on November 28, 2019 by xi'an

A new arXival by Pierre Monteiller et al. on resolving label switching by optimal transport. To appear in NeurIPS 2019, next month (where I will be, but extra muros, as I have not registered for the conference). Among other things, the paper was inspired from an answer of mine on X validated, presumably a première (and a dernière?!). Rather than picketing [in the likely unpleasant weather ]on the pavement outside the conference centre, here are my raw reactions to the proposal made in the paper. (Usual disclaimer: I was not involved in the review of this paper.)

“Previous methods such as the invariant losses of Celeux et al. (2000) and pivot alignments of Marin et al. (2005) do not identify modes in a principled manner.”

Unprincipled, me?! We did not aim at identifying all modes but only one of them, since the posterior distribution is invariant under reparameterisation. Without any bad feeling (!), I still maintain my position that using a permutation invariant loss function is a most principled and Bayesian approach towards a proper resolution of the issue. Even though figuring out the resulting Bayes estimate may prove tricky.

The paper thus adopts a different approach, towards giving a manageable meaning to the average of the mixture distributions over all permutations, not in a linear Euclidean sense but thanks to a Wasserstein barycentre. Which indeed allows for an averaged mixture density, although a point-by-point estimate that does not require switching to occur at all was already proposed in earlier papers of ours. Including the Bayesian Core. As shown above. What was first unclear to me is how necessary the Wasserstein formalism proves to be in this context. In fact, the major difference with the above picture is that the estimated barycentre is a mixture with the same number of components. Computing time? Bayesian estimate?

Green’s approach to the problem via a point process representation [briefly mentioned on page 6] of the mixture itself, as for instance presented in our mixture analysis handbook, should have been considered. As well as issues about Bayes factors examined in Gelman et al. (2003) and our more recent work with Kate Jeong Eun Lee. Where the practical impossibility of considering all possible permutations is processed by importance sampling.

An idle thought that came to me while reading this paper (in Seoul) was that a more challenging problem would be to face a model invariant under the action of a group with only a subset of known elements of that group. Or simply too many elements in the group. In which case averaging over the orbit would become an issue.

at the centre of Bayes

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , on October 14, 2019 by xi'an

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