Archive for Mark Beaumont

Mark [a]B[c], plus cats

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , on June 22, 2024 by xi'an

30 May was a day of first and last times, if not in capital ways (for me), on the One World ABC webinar. This was the first time we had a talk by Mark Beaumont and also the first time I had team experience of facing a smoking participant, while this was the last time of our monthly webinar for the (Northern) academic year.

The talk was about model misspecification in population genomic, from an ABC perspective with the motivation of common noticeable difference between the distributions of d(s,s⁰) and d(s,s’), distances between the prior predictively simulated summary statistics and the observed ones vs posterior generated ones, which should indicates misspecification, esp with complicated models. Mark and his coauthors then supported a gradual elimination of summary statistics to diminish the discrepancy, hence voluntarily impoverishing the model. While blaming the statistics sounded a bit like shooting the messenger, the resolution is of obvious interest if backing from modelling the misspecification itself.

Some of the presented work was conducted in Ward et al (2022, NeurIPS) with a reference to the outlying Ratmann et al (2009) we later discussed, for including tolerance as an extra parameter ε, thereby reconsidering Wikinson’s exact ABC for noisy observations y by the medium of a normalising flow on the marginal distribution of the denoised x (learned from the prior predictive)

More precisely, the idea is to drop summary statistics by checking whether or not the observed S⁰ belongs to HPD region, removing one component of S at a time, using e.g. a k-NN estimate for the summary density (hence depending on parameterisation of said statistics for the distance). Hopefully, the process stops before loosing identifiability by using too few statistics. I also wondered at multiple uses of the data in this sequential procedure but Mark argued for adopting a meta- or pragma- or Gelmanian- Bayesian perspective in the end!

Another perk was the appearance of (and illustration with) the Scottish Wildcat, mentioned in The Guardian a few months ago and discussed in the ‘Og, with further papers exploring more aspects of this hybridization, like a posterior applied to a much more complex phylogenic tree reconstruction for cats of different creeds and many related parameters.

Marc Beaumont on One World ABC webinar [30 May, 9am]

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on May 17, 2024 by xi'an

For the final talk of this Spring season of the One World ABC webinar, we are very glad to welcome Marc Beaumont, a central figure in the development of ABC methods and inference! (And a coauthor of our ABC-PMC paper.)

Model misspecification in population genomic
Mark Beaumont
University of Bristol
30th May 2024, 9.00am UK time

Abstract
In likelihood-free settings, problematic effects of model misspecification can manifest themselves during computation, leading to nonsensical answers, particularly causing convergence problems in sequential algorithms. This issue has been well studied in the last 10 years, leading to a number of methods for robust inference. In practical applications, likelihood-free methods tend to be applied to the output of complex simulations where there is a choice of summary statistics that can be computed. One approach to handling misspecification is to simply not use summary statistics computed from simulations of the model under the prior that cannot be with those observed in the data. This presentation gives a brief review of methods for observing and handling misspecification in ABC and SBI, and then discusses approaches that we have explored in a population genomic modelling framework.

unscrambling Scottish eggs for wildcats

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , on January 17, 2024 by xi'an


Read in Nature (and The Guardian) a report on the attempt to rewild the Scottish wildcat population due to its current high levels of domestic-cat DNA resulting from decades of interbreeding with domestic cats gone feral, even though this happened only relatively recently, in the 1960′. Thanks, rather paradoxically, to the 150 captive wildcats found in zoos and parks across the UK, more preserved from this interbreeding, by selective breeding towards removing hybrid segments of DNA from the genome of the kittens. The attempt is operated by Saving Wildcats, who have already released 19 wildcats in the Cairngorms.

“However, the project will not be a quick or an easy one”, said programme scientist Prof Mark Beaumont of Bristol University. “The idea would be to have funds to monitor what is happening in the kittens as you progress, and that costs money. A whole genome of an individual cat sets you back about $200.” The Guardian Sun 10 Dec 2023

The project is backed up by a scientific analysis of the chances of rewilding, written by Daniel J. Lawson, Jo Howard-McCombe, Mark Beaumont, and Helen Senn. (While the paper includes a modelling of the meiosis process via a simulation experiment, no ABC were harmed in running it!)

Approximation Methods in Bayesian Analysis [#2]

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , on June 22, 2023 by xi'an

A more theoretical Day #2 of the workshop, with Debdeep Pati comparing two representations of Gaussian processes with significantly different efficiencies, and Aad van der Vaart presenting a form of linearisation for a range of inverse problems, Kolyan Ray debiasing Lasso impacts by variational Bayes, although through a somewhat intricate process that distanced the procedure from Bayesian grounds imho, Judith Rousseau (Dauphine) also drifting away from Bayesian canons by looking anew at empirical Bayes with surprising differences from genuine B analysis, connecting with the cutoff phenomenon she and Kerrie exhibited in their 2011 mixture paper, as well as labelling the marginal likelihood a misspecified model. Trevor Campbell and Sinead Williamson both provided Bayesian perspectives on normalising flows, in particular the impact of computer imprecision on reversibility, leading to the notion of shadow paths (screenshot below), while Giovanni Rebaudo talked about mixtures supported by trees, a fascinating object!

On Day #3, Marc Beaumont talked on a mixture of composite likelihood à la Ryden, making me wonder of optimisation of blocks for HMC? EP-ABC, with the issue of the unknown amount of approximation, and adaptivity?, Maria de Iorio presented work on finite and infinite mixtures with repulsive (Coulomb) priors, achieving a unified framework, plus known evidence (?), with a correlated talk by Federico Camerlenghi in the afternoon, with novel notions (for me) of Palm measures and calculus, and another correlated talk by María-Fernanda Gil Leyva Villa, on stick-breaking processes for species sampling with dependent length variables, with related improvements in Gibbs implementation (screenshot below).
This was followed by two theoretical talks on continuous time processes by Paul Jenkins (Warwick) on the fine properties of the Flemming-Viot process, with mentions of Don Dawson’s results reminding me of the 1988 and 1989 summers I spent at Carleton University, where he was located at the time, and Matteo Ruggieri, with the novel (to me) notion of dual Markov processes that could prove useful in a lot of latent variable models. Fabrizio Leisen expanded on his early work on partial exchangeability and Steve MacEachern on dependent quantile pyramids, which relate to quantile regression, a constant source of puzzlement for me. Motivating the perspective by robustness and misspecification arguments. But I am a wee bit puzzled by the distinction between quantile pyramids and other non-parametric solutions.

On the outdoor front (in early mornings), choppy waters at sea (in the Sugiton calanque, pictured above) thanks to the endless mistral wind, nice run down from Mont Puget with friends, limited utility of my rented mountain bike (except to reach the nearest supermarket, 3km away)

research position in Bristol

Posted in pictures, Statistics, University life with tags , , , , , , , , , on September 6, 2019 by xi'an

Christophe Andrieu is seeking a senior research associate (reference ACAD103715) at the University of Bristol to work on new approaches to Bayesian data science. The selected candidate would work with Prof. Christophe Andrieu (School of Mathematics) and Prof. Mark Beaumont (Life Science) on new approaches to tackle Bayesian inference in complex statistical models arising in particular in the area of Health Science, with a focus on genetics and/or epidemiological aspects. The position is associated with a £3M programme funded by EPSRC, Bayes4Health, and brings together research groups from the Universities of Lancaster, Bristol, Cambridge, Oxford and Warwick. Active collaboration across the partner institutions, other project partners and the programme grant CoSInES is expected. The position is for up to four years.

The position is for a duration of four years and interviews will take place in early October. Applicants with strong methodological and computational skills and are looking to put together a team of researchers with skills that cover theoretical, methodological and applied statistics should contact Christophe Andrieu at the earliest.