Archive for likelihood-free methods

PhD position for research in ABC in Chalmers University

Posted in Statistics with tags , , , , , , , , , on May 27, 2020 by xi'an

[Posting a call for PhD candidates from Umberto Piccini as the deadline is June 1, next Monday!]

A PhD student position in mathematical statistics on simulation-based inference methods for models with an “intractable” likelihood is available at the Dept. Mathematical Sciences, Chalmers University, Gothenburg (Sweden).

You will be part of an international collaboration to create new methodology bridging between simulation-based inference (such as approximate Bayesian computation and other likelihood-free methods) and deep neuronal networks. The goal is to ease inference for stochastic modelling.

Details on the project and the essential requirements are at https://www.chalmers.se/en/departments/math/research/research-groups/AIMS/Pages/ai-project-5.aspx

The PhD student position is fully funded and is up to 5 years, in the dynamic and international city of Gothenburg, the second largest city in Sweden, https://www.goteborg.com/en/ As a PhD student in Mathematical Sciences you will have opportunities for many inspiring conversations, a lot of autonomous work and some travel.

The position will be supervised by Assoc. Prof. Umberto Picchini.

Apply by 01 June 2020 following the instructions at
https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=8556

For informal enquiries, please get in touch with Umberto Picchini

another mirror of ABC in Gre[e]noble

Posted in Statistics with tags , , , , , , , , , , on March 3, 2020 by xi'an

There will now be a second mirror workshop of ABC in Grenoble. Taking place at the Université de Montpellier, more precisely at the Alexander Grothendieck Montpellier Institute, Building 9, room 430 (4th floor), Triolet Campus. It is organised by my friend Jean-Michel Marin. Great to see a mirror at one of the major breeding places of ABC, where I personally heard of ABC for the first time and met several of the main A[B]Ctors..! The dates are 19-20 March, with talks transmitted from 9am to 5am [GMT+1]. Since the video connection can accommodate 1918 more mirrors, if anyone else is interested in organising another mirror, please contact me for technical details.

in Bristol for the day

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on February 28, 2020 by xi'an

I am in Bristol for the day, giving a seminar at the Department of Statistics where I had not been for quite a while (and not since the Department has moved to a beautifully renovated building). The talk is on ABC-Gibbs, whose revision is on the verge of being resubmitted. (I also hope Greta will let me board my plane tonight…)

mirror of ABC in Grenoble

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , on February 26, 2020 by xi'an

I am quite glad to announce that there will definitely be at least one mirror workshop of ABC in Grenoble. Taking place at Warwick University, in the Zeeman building (room MS0.05) and organised by my colleague Rito Dutta. The dates are 19-20 March, with talks transmitted from 9am to 5am [GMT+1]. Since the video connection can accommodate 19 more mirrors, if anyone is interested in organising an other mirror, please contact me for technical details.

Bayesian inference with no likelihood

Posted in Books, Statistics, University life with tags , , , , , , , , on January 28, 2020 by xi'an

This week I made a quick trip to Warwick for the defence (or viva) of the PhD thesis of Jack Jewson, containing novel perspectives on constructing Bayesian inference without likelihood or without complete trust in said likelihood. The thesis aimed at constructing minimum divergence posteriors in an M-open perspective and built a rather coherent framework from principles to implementation. There is a clear link with the earlier work of Bissiri et al. (2016), with further consistency constraints where the outcome must recover the true posterior in the M-closed scenario (if not always the case with the procedures proposed in the thesis).

Although I am partial to the use of empirical likelihoods in setting, I appreciated the position of the thesis and the discussion of the various divergences towards the posterior derivation (already discussed on this blog) , with interesting perspectives on the calibration of the pseudo-posterior à la Bissiri et al. (2016). Among other things, the thesis pointed out a departure from the likelihood principle and some of its most established consequences, like Bayesian additivity. In that regard, there were connections with generative adversarial networks (GANs) and their Bayesian versions that could have been explored. And an impression that the type of Bayesian robustness explored in the thesis has more to do with outliers than with misspecification. Epsilon-contamination amodels re quite specific as it happens, in terms of tails and other things.

The next chapter is somewhat “less” Bayesian in my view as it considers a generalised form of variational inference. I agree that the view of the posterior as a solution to an optimisation is tempting but changing the objective function makes the notion less precise.  Which makes reading it somewhat delicate as it seems to dilute the meaning of both prior and posterior to the point of becoming irrelevant.

The last chapter on change-point models is quite alluring in that it capitalises on the previous developments to analyse a fairly realistic if traditional problem, applied to traffic in London, prior and posterior to the congestion tax. However, there is always an issue with robustness and outliers in that the notion is somewhat vague or informal. Things start clarifying at the end but I find surprising that conjugates are robust optimal solutions since the usual folk theorem from the 80’s is that they are not robust.