Archive for discrepancy

Concentration and robustness of discrepancy-based ABC [One World ABC ‘minar, 28 April]

Posted in Statistics, University life with tags , , , , , , , , , , , on April 15, 2022 by xi'an

Our next speaker at the One World ABC Seminar will be Pierre Alquier, who will talk about “Concentration and robustness of discrepancy-based ABC“, on Thursday April 28, at 9.30am UK time, with an abstract reported below.
Approximate Bayesian Computation (ABC) typically employs summary statistics to measure the discrepancy among the observed data and the synthetic data generated from each proposed value of the parameter of interest. However, finding good summary statistics (that are close to sufficiency) is non-trivial for most of the models for which ABC is needed. In this paper, we investigate the properties of ABC based on integral probability semi-metrics, including MMD and Wasserstein distances. We exhibit conditions ensuring the contraction of the approximate posterior. Moreover, we prove that MMD with an adequate kernel leads to very strong robustness properties.

efficient acquisition rules for ABC

Posted in pictures, Statistics, University life with tags , , , , , , , , on June 5, 2017 by xi'an

A few weeks ago, Marko Järvenpää, Michael Gutmann, Aki Vehtari and Pekka Marttinen arXived a paper on sampling design for ABC that reminded me of presentations Michael gave at NIPS 2014 and in Banff last February. The main notion is that, when the simulation from the model is hugely expensive, random sampling does not make sense.

“While probabilistic modelling has been used to accelerate ABC inference, and strategies have been proposed for selecting which parameter to simulate next, little work has focused on trying to quantify the amount of uncertainty in the estimator of the ABC posterior density itself.”

The above question  is obviously interesting, if already considered in the literature for it seems to focus on the Monte Carlo error in ABC, addressed for instance in Fearnhead and Prangle (2012), Li and Fearnhead (2016) and our paper with David Frazier, Gael Martin, and Judith Rousseau. With corresponding conditions on the tolerance and the number of simulations to relegate Monte Carlo error to a secondary level. And the additional remark that the (error free) ABC distribution itself is not the ultimate quantity of interest. Or the equivalent (?) one that ABC is actually an exact Bayesian method on a completed space.

The paper initially confused me for a section on the very general formulation of ABC posterior approximation and error in this approximation. And simulation design for minimising this error. It confused me as it sounded too vague but only for a while as the remaining sections appear to be independent. The operational concept of the paper is to assume that the discrepancy between observed and simulated data, when perceived as a random function of the parameter θ, is a Gaussian process [over the parameter space]. This modelling allows for a prediction of the discrepancy at a new value of θ, which can be chosen as maximising the variance of the likelihood approximation. Or more precisely of the acceptance probability. While the authors report improved estimation of the exact posterior, I find no intuition as to why this should be the case when focussing on the discrepancy, especially because small discrepancies are associated with parameters approximately generated from the posterior.

Le Monde sans puzzle #933

Posted in Books, Kids, Statistics, University life with tags , , , , , on October 17, 2015 by xi'an

While Le Monde mathematical puzzle is purely geometric this week

If twelve points in a plane are such that, for any 5-uplet of those, at least 4 are on the same circle, and if M is the largest number of those points on the same circle, what is the minimal value of M?

and not straightforward to solve with an R code, there are several entries of interest in the Sciences and Medicine leaflet. One about capture-mark-recapture: making fun of a PLOS One paper on a capture-recapture study about the movements of bed bugs in New Jersey apartments. Another one on the resolution by Terry Tao of Erdös’ discrepancy conjecture. Which states that. for any (deterministic) sequence f:N{1,+1} taking values in {1,+1}, the discrepancy of f is infinite, when the discrepancy is defined as

\sup_{n,d} \left|\sum_{j=1}^n f(jd)\right|

The entry in Le Monde tells the story of the derivation of the result and in particular the role of the Polymath5 project launched by Tao. It is interesting it is such a hard problem when considering the equivalent for a random sequence, which is more or less the gambler’s ruin result of Huygens. And a third entry on the explosion of the predatory journals, which publish essentially every submission in open access provided the authors accept to pay “charges”. And borrow titles and formats from existing reviews to a point where they can fool authors…

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