Archive for Queensland University of Technology

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!)

Nested Sampling SMC [a reply]

Posted in Books, Statistics, University life with tags , , , , , , , , , on April 9, 2020 by xi'an
Here is a response from Robert Salomone following my comments of the earlier day (and pointing out I already commented the paper two years ago):
You may be interested to know that we are at the tail end of carrying out a major revision of the paper, which we hope will be done in the near future — there will be some new theory (we are in the final stages for a consistency proof of the ANS-SMC algorithm with new co-author Adam Johansen), as well as new numerics (including comparisons to Nested Sampling), and additional discussion that clarifies the overall narrative.
A few comments relating your post that may clear some things up:
  • The method you describe with the auxiliary variable is actually one of three proposed algorithms. We call this one “Improved Nested Sampling” as it is the algorithm most similar to the original Nested Sampling. Two further extensions are the adaptive SMC sampler, and the fixed SMC sampler – the latter of which is provably consistent and unbiased for the model evidence (we also often see improvements over standard NS for similar computational effort when MCMC is used).
  • Regarding computational effort – it is the same for Improved NS (in fact, you can obtain the standard Nested Sampling evidence estimate from the same computational run!). For the adaptive variant, the computational effort is roughly the same for ρ = e⁻¹. In the current version of the paper this is only discussed briefly (last page of p.23). However, in the revision we will include additional experiments comparing the practical performance.
  • Regarding the question of “why not regular SMC”; we chose to focus more on why SMC is a good way to do Nested Sampling rather than why Nested Sampling is a good way to do SMC. Our main priority was to show there is a lot of opportunity to develop new nested sampling style algorithms by approaching it from a different angle. That said, Nested Sampling’s primary advantage over standard SMC seems to be in problems involving “phase transitions’’ such as our first example, for which temperature based methods are inherently ill-suited (and will often fail to detect so!).

nested sampling via SMC

Posted in Books, pictures, Statistics with tags , , , , , , , , , , , , on April 2, 2020 by xi'an

“We show that by implementing a special type of [sequential Monte Carlo] sampler that takes two im-portance sampling paths at each iteration, one obtains an analogous SMC method to [nested sampling] that resolves its main theoretical and practical issues.”

A paper by Queenslander Robert Salomone, Leah South, Chris Drovandi and Dirk Kroese that I had missed (and recovered by Grégoire after we discussed this possibility with our Master students). On using SMC in nested sampling. What are the difficulties mentioned in the above quote?

  1. Dependence between the simulated samples, since only the offending particle is moved by one or several MCMC steps. (And MultiNest is not a foolproof solution.)
  2. The error due to quadrature is hard to evaluate, with parallelised versions aggravating the error.
  3. There is a truncation error due to the stopping rule when the exact maximum of the likelihood function is unknown.

Not mentioning the Monte Carlo error, of course, which should remain at the √n level.

“Nested Sampling is a special type of adaptive SMC algorithm, where weights are assigned in a suboptimal way.”

The above remark is somewhat obvious for a fixed sequence of likelihood levels and a set of particles at each (ring) level. moved by a Markov kernel with the right stationary target. Constrained to move within the ring, which may prove delicate in complex settings. Such a non-adaptive version is however not realistic and hence both the level sets and the stopping rule need be selected from the existing simulation, respectively as a quantile of the observed likelihood and as a failure to modify the evidence approximation, an adaptation that is a Catch 22! as we already found in the AMIS paper.  (AMIS stands for adaptive mixture importance sampling.) To escape the quandary, the authors use both an auxiliary variable (to avoid atoms) and two importance sampling sequences (as in AMIS). And only a single particle with non-zero incremental weight for the (upper level) target. As the full details are a bit fuzzy to me, I hope I can experiment with my (quarantined) students on the full implementation of the method.

“Such cases asides, the question whether SMC is preferable using the TA or NS approach is really one of whether it is preferable to sample (relatively) easy distributions subject to a constraint or to sample potentially difficult distributions.”

A question (why not regular SMC?) I was indeed considering until coming to the conclusion section but did not find it treated in the paper. There is little discussion on the computing requirements either, as it seems the method is more time-consuming than a regular nested sample. (On the personal side,  I appreciated very much their “special thanks to Christian Robert, whose many blog posts on NS helped influence this work, and played a large partin inspiring it.”)

positions at QUT stats

Posted in Statistics with tags , , , , , , , , on September 4, 2017 by xi'an

Chris Drovandi sent me the information that the Statistics GroupQUT, Brisbane, is advertising for three positions:

This is a great opportunity, a very active group, and a great location, which I visited several times, so if interested apply before October 1.

Bayes on the beach [and no bogus!]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , on July 27, 2016 by xi'an

Bayes on the Beach is a yearly conference taking place in Queensland Gold Coast and organised by Kerrie Mengersen and her BRAG research group at QUT. To quote from the email I just received, the conference will be held at the Mantra Legends Hotel on Surfers Paradise, Gold Coast during November 7 – 9, 2016. The conference provides a forum for discussion on developments and applications of Bayesian statistics, and includes keynote presentations, tutorials, practical problem-based workshops, invited oral presentations, and poster presentations. Abstract submissions are now open until September 2.