Archive for England

objective and subjective RSS Read Paper next week

Posted in Books, pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , on April 5, 2017 by xi'an

Andrew Gelman and Christian Hennig will give a Read Paper presentation next Wednesday, April 12, 5pm, at the Royal Statistical Society, London, on their paper “Beyond subjective and objective in statistics“. Which I hope to attend and else to write a discussion. Since the discussion (to published in Series A) is open to everyone, I strongly encourage ‘Og’s readers to take a look at the paper and the “radical” views therein to hopefully contribute to this discussion. Either as a written discussion or as comments on this very post.

The Hanging Tree

Posted in Books, Kids, Travel with tags , , , , , , , on March 25, 2017 by xi'an

This is the fifth sixth volume of Ben Aaronovitch’s Rivers of London series. Which features PC Peter Grant from the London’s Metropolitan Police specialising in paranormal crime. Joining a line of magicians that was started by Isaac Newton. And with the help of water deities. Although this English magic sleuthing series does not compare with the superlative Jonathan Strange & Mr. Norrell single book, The Hanging Tree remains highly enjoyable, maybe more for its style and vocabulary than for the detective story itself, which does not sound completely coherent (unless I read it too quickly during the wee hours in Banff last week). And does not bring much about this part of London. Still a pleasure to read as the long term pattern of Aaronovitch’s universe slowly unravels and some characters get more substance and depth.

Peter Lee (1940?-2017)

Posted in Books, pictures, R, Statistics, University life, Wines with tags , , , , , , on March 12, 2017 by xi'an

Just heard the sad news that Peter Lee, British Bayesian and author of Bayesian Statistics: An Introduction, has passed away yesterday night. While I did not know him, I remember meeting him at a few conferences in the UK and spending an hilarious evening at the pub. When the book came out, I thought it was quite fine an introduction to Bayesian Statistics, with enough mathematical details and prerequisites to make it worthwhile studying, while also including computational recommendations. Fare thee well, Peter.

Oxford snapshot [jatp]

Posted in Books, Kids, pictures, Travel, University life with tags , , , , , , on February 9, 2017 by xi'an

non-reversible Langevin samplers

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , on February 6, 2017 by xi'an

In the train to Oxford yesterday night, I read through the recently arXived Duncan et al.’s Nonreversible Langevin Samplers: Splitting Schemes, Analysis and Implementation. Standing up the whole trip in the great tradition of British trains.

The paper is fairly theoretical and full of Foster-Lyapunov assumptions but aims at defending an approach based on a non-reversible diffusion. One idea is that the diffusion based on the drift {∇ log π(x) + γ(x)} is associated with the target π provided

∇ . {π(x)γ(x)} = 0

which holds for the Langevin diffusion when γ(x)=0, but produces a non-reversible process in the alternative. The Langevin choice γ(x)=0 happens to be the worst possible when considering the asymptotic variance. In practice however the diffusion need be discretised, which induces an approximation that may be catastrophic for convergence if not corrected, and a relapse into reversibility if corrected by Metropolis. The proposal in the paper is to use a Lie-Trotter splitting I had never heard of before to split between reversible [∇ log π(x)] and non-reversible [γ(x)] parts of the process. The deterministic part is chosen as γ(x)=∇ log π(x) [but then what is the point since this is Langevin?] or as the gradient of a power of π(x). Although I was mostly lost by that stage, the paper then considers the error induced by a numerical integrator related with this deterministic part, towards deriving asymptotic mean and variance for the splitting scheme. On the unit hypercube. Although the paper includes a numerical example for the warped normal target, I find it hard to visualise the implementation of this scheme. Having obviously not heeded Nicolas’ and James’ advice, the authors also analyse the Pima Indian dataset by a logistic regression!)

back in Oxford

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , on January 30, 2017 by xi'an

As in the previous years, I am back in Oxford (England) for my short Bayesian Statistics course in the joint Oxford-Warwick PhD programme, OxWaSP.  For some unclear reason, presumably related to the Internet connection from Oxford, I have not been able to upload my slides to Slideshare, so here the [99.9% identical] older version:

learning and inference for medical discovery in Oxford [postdoc]

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , on January 10, 2017 by xi'an

[Here is a call for a two-year postdoc in Oxford sent to me by Arnaud Doucet. For those worried about moving to Britain, I think that, given the current pace—or lack thereof—of the negotiations with the EU, it is very likely that Britain will not have Brexited two years from now.]

Numerous medical problems ranging from screening to diagnosis to treatment of chronic diseases to  management of care in hospitals requires the development of novel statistical models and methods. These models and methods need to address the unique characteristics of medical data such as sampling bias, heterogeneity, non-stationarity, informative censoring etc. Existing state-of-the-art machine learning and statistics techniques often fail to exploit those characteristics. Additionally, the focus needs to be on probabilistic models which are
interpretable by the clinicians so that the inference results can be integrated within the medical-decision making.

We have access to unique datasets for clinical deterioration of patients in the hospital, for cancer screening, and for treatment of chronic diseases. Preliminary work has been tested and implemented at UCLA Medical Center, resulting in significantly management care in this hospital.

The successful applicant will be expected to develop new probabilistic models and learning methods inspired by these applications. The focus will be primarily on methodological and theoretical developments, and involve collaborating with Oxford researchers in machine learning, computational statistics and medicine to bring these developments to practice.

The post-doctoral researcher will be jointly supervised by Prof. Mihaela van der Schaar and Prof. Arnaud Doucet. Both of them have a strong track-record in advising PhD students and post-doctoral researchers who subsequently became successful academics in statistics, engineering sciences, computer science and economics. The position is for 2 years.