Archive for Errol Street

scalable Langevin exact algorithm [armchair Read Paper]

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

So, Murray Pollock, Paul Fearnhead, Adam M. Johansen and Gareth O. Roberts presented their Read Paper with discussions on the Wednesday aft! With a well-sized if virtual audience of nearly a hundred people. Here are a few notes scribbled during the Readings. And attempts at keeping the traditional structure of the meeting alive.

In their introduction, they gave the intuition of a quasi-stationary chain as the probability to be in A at time t while still alice as π(A) x exp(-λt) for a fixed killing rate λ. The concept is quite fascinating if less straightforward than stationarity! The presentation put the stress on the available recourse to an unbiased estimator of the κ rate whose initialisation scaled as O(n) but allowed a subsampling cost reduction afterwards. With a subsampling rat connected with Bayesian asymptotics, namely on how quickly the posterior concentrates. Unfortunately, this makes the practical construction harder, since n is finite and the concentration rate is unknown (although a default guess should be √n). I wondered if the link with self-avoiding random walks was more than historical.

The initialisation of the method remains a challenge in complex environments. And hence one may wonder if and how better it does when compared with SMC. Furthermore, while the motivation for using a Brownian motion stems from the practical side, this simulation does not account for the target π. This completely blind excursion sounds worse than simulating from the prior in other settings.

One early illustration for quasi stationarity was based on an hypothetical distribution of lions and wandering (Brownian) antelopes. I found that the associated concept of soft killing was not necessarily well received by …. the antelopes!

As it happens, my friend and coauthor Natesh Pillai was the first discussant! I did no not get the details of his first bimodal example. But he addressed my earlier question about how large the running time T should be. Since the computational cost should be exploding with T. He also drew a analogy with improper posteriors as to wonder about the availability of convergence assessment.

And my friend and coauthor Nicolas Chopin was the second discussant! Starting with a request to… leave the Pima Indians (model)  alone!! But also getting into a deeper assessment of the alternative use of SMCs.

London snapshot [jatp]

Posted in pictures, Running, Statistics, Travel with tags , , , , , , on April 13, 2017 by xi'an

beyond objectivity, subjectivity, and other ‘bjectivities

Posted in Statistics with tags , , , , , , , , , , , , , on April 12, 2017 by xi'an

Here is my discussion of Gelman and Hennig at the Royal Statistical Society, which I am about to deliver!

Discussions on semi-automatic ABC [deadline]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , on January 4, 2012 by xi'an

The deadline for the discussions on the talk by Paul Fearnhead and Dennis Prangle is next Monday, so all potential discussants are invited to send their 400 word piece to the Royal Statistical Society. I have now written two discussions along most of the points I had prepared, for my oral discussion.  I am now collecting written contributions from different authors to compile an arXiv document as on earlier occasions.

Riemann, Hamilton, Lagrange and others

Posted in Statistics, Travel, University life with tags , , , , , , , , , on October 14, 2010 by xi'an

Today, I took part in the Read Paper session of the Royal Statistical Society, first by presenting an overview of MCMC methods, second by giving a short discussion on the paper by Mark Girolami and Ben Calderhead. The pre-ordinary as well as the ordinary sessions were very well-attended and it is a real pity that this was the first instance I attended when the talk was not given in the main lecture room. (Which, sadly enough, was already booked.) Instead, the meeting took place in the twice-as-small Council room which means people had to remain standing for the whole session… Anyhow, Mark Girolami gave two great talks where the geometric intuition was predominant. The following 13 oral discussions were quite diverse, from machine learning to Bayesian model choice, to infinite dimensional simulation and I am convinced the written discussion will be even richer.  (Discussions have to be sent before October 27.) Here are my own slides focussing on the discretisation issue.

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