Archive for University of Warwick

accronyms [CDT lectures]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , on May 16, 2022 by xi'an

This week, I gave a short and introductory course in Warwick for the CDT (PhD) students on my perceived connections between reverse logistic regression à la Geyer and GANS, among other things. The first attempt was cancelled in 2020 due to the pandemic, the second one in 2021 was on-line and thus offered little possibilities for interactions. Preparing for this third attempt made me read more papers on some statistical analyses of GANs and WGANs, which was more satisfactory [for me] even though I could not get into the technical details…

REF 2021

Posted in Statistics, University life with tags , , , , , , , , , on May 14, 2022 by xi'an

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.

crash

Posted in pictures, Travel, University life with tags , , , , , , on April 2, 2022 by xi'an

robustified Hamiltonian

Posted in Books, Statistics, University life with tags , , , , , , , , , on April 1, 2022 by xi'an

In Gregynog, last week, Lionel Riou-Durant (Warwick) presented his recent work with Jure Vogrinc on Metropolis Adjusted Langevin Trajectories, which I had also heard in the Séminaire Parisien de Statistique two weeks ago. Starting with a nice exposition of Hamiltonian Monte Carlo, highlighting its drawbacks. This includes the potentially damaging impact of poorly tuning the integration time. Their proposal is to act upon the velocity in the Hamiltonian through Langevin (positive) damping, which also preserves the stationarity.  (And connects with randomised HMC.) One theoretical in the paper is that the Langevin diffusion achieves the fastest mixing rate among randomised HMCs. From a practical perspective, there exists a version of the leapfrog integrator that adapts to this setting and can be implemented as a Metropolis adjustment. (Hence the MALT connection.) An interesting feature is that the process as such is ergodic, which avoids renewal steps (and U-turns). (There are still calibration parameters to adjust, obviously.)

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