Archive for Australia

likelihood-free and summary-free?

Posted in Books, Mountains, pictures, Statistics, Travel with tags , , , , , , , , , , , , , on March 30, 2021 by xi'an

My friends and coauthors Chris Drovandi and David Frazier have recently arXived a paper entitled A comparison of likelihood-free methods with and without summary statistics. In which they indeed compare these two perspectives on approximate Bayesian methods like ABC and Bayesian synthetic likelihoods.

“A criticism of summary statistic based approaches is that their choice is often ad hoc and there will generally be an  inherent loss of information.”

In ABC methods, the recourse to a summary statistic is often advocated as a “necessary evil” against the greater evil of the curse of dimension, paradoxically providing a faster convergence of the ABC approximation (Fearnhead & Liu, 2018). The authors propose a somewhat generic selection of summary statistics based on [my undergrad mentors!] Gouriéroux’s and Monfort’s indirect inference, using a mixture of Gaussians as their auxiliary model. Summary-free solutions, as in our Wasserstein papers, rely on distances between distributions, hence are functional distances, that can be seen as dimension-free as well (or criticised as infinite dimensional). Chris and David consider energy distances (which sound very much like standard distances, except for averaging over all permutations), maximum mean discrepancy as in Gretton et al. (2012), Cramèr-von Mises distances, and Kullback-Leibler divergences estimated via one-nearest-neighbour formulas, for a univariate sample. I am not aware of any degree of theoretical exploration of these functional approaches towards the precise speed of convergence of the ABC approximation…

“We found that at least one of the full data approaches was competitive with or outperforms ABC with summary statistics across all examples.”

The main part of the paper, besides a survey of the existing solutions, is to compare the performances of these over a few chosen (univariate) examples, with the exact posterior as the golden standard. In the g & k model, the Pima Indian benchmark of ABC studies!, Cramèr does somewhat better. While it does much worse in an M/G/1 example (where Wasserstein does better, and similarly for a stereological extremes example of Bortot et al., 2007). An ordering inversed again for a toad movement model I had not seen before. While the usual provision applies, namely that this is a simulation study on unidimensional data and a small number of parameters, the design of the four comparison experiments is very careful, eliminating versions that are either too costly or too divergence, although this could be potentially criticised for being unrealistic (i.e., when the true posterior is unknown). The computing time is roughly the same across methods, which essentially remove the call to kernel based approximations of the likelihood. Another point of interest is that the distance methods are significantly impacted by transforms on the data, which should not be so for intrinsic distances! Demonstrating the distances are not intrinsic…

ABC not in Svalbard [this time!]

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , on January 28, 2021 by xi'an

Alas, thrice alas!, there will be no one attending the ABC in Svalbard in Svalbard  next April. As the travel conditions to and around Norway are getting tougher, it is just too unrealistic to expect traveling to the Far North even from Oslo. Too bad, but hopefully there will be another opportunity in a near enough future…

However, don’t give up the fight!, the mirror meetings in Brisbane and Grenoble are still planned to take place, along with an on-line version accommodating most of the speakers invited so far. Anyone interested in holding another mirror meeting?! Please contact me.

ABC in Svalbard [update]

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on December 16, 2020 by xi'an

Even though no one can tell at this stage who will be allowed to travel to Svalbard mid April 2021, we are keeping the workshop to physically take place as planned in Longyearbyen. With at least a group of volunteers made of researchers from Oslo (since at the current time, travel between mainland Norway and Svalbard is authorised). The conference room reservation has been confirmed yesterday and there are a few hotel rooms pre-booked through Anyone planning to attend just need to (i) register on the workshop webpage, (ii) book an hotel room for the duration of the workshop (or more)., and (iii) reserve a plane ticket as there are not that many flights planned.

Obviously this option should only attract a few brave souls (from nearby countries). We are thus running at the same time three mirror workshops in Brisbane (QUT), Coventry (University of Warwick), and Grenoble (IMAG & INRIA). Except for Warwick, where the current pandemic restrictions do not allow for a workshop to take place, the mirror workshops will take place in university buildings and be face-to-face (with video connections as well). Julyan Arbel has set-up a mirror webpage as well. With a (free) registration deadline of 31 March, the workshop being open to all who can attend. Hopefully enough of us will gather here or there to keep up with the spirit of the earlier ABC workshops. (To make the mirror places truly ABCesque, it should have been set in A as Autrans rather than Grenoble!)

computing Bayes 2.0

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on December 11, 2020 by xi'an

Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)

Data Science & Machine Learning book free for download

Posted in Statistics with tags , , , , , , , on November 30, 2020 by xi'an