Archive for Imperial College London

the exponential power of now

Posted in Books, Statistics, University life with tags , , , , , , , , , , on March 22, 2020 by xi'an

The New York Times had an interview on 13 March with Britta Jewell (MRC, Imperial College London) and Nick Jewell (London School of Hygiene and Tropical Medicine & U of C Berkeley), both epidemiologists. (Nick is also an AE for Biometrika.) Where they explain quite convincingly that the devastating power of the exponential growth and the resulting need for immediate reaction. An urgency that Western governments failed to heed, unsurprisingly including the US federal government. Maybe they should have been told afresh about the legend of paal paysam, where the king who lost to Krishna was asked to double rice grains on the successive squares of a chess board. (Although this is presumably too foreign a thought experiment for The agent orange. He presumably prefers the unbelievable ideological rantings of John Ioannides. Who apparently does mind sacrificing “people with limited life expectancies” for the sake of the economy.) Incidentally, I find the title “The exponential power of now” fabulous!

lecturer position in Data Centric Engineering and Statistics, Imperial College London

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , on April 2, 2018 by xi'an

My friend and Warwick colleague Mark Girolami sent me this announcement for a permanent Lecturer position at Imperial [College London], funded by his recent research chair by the Royal Academy of Engineering (congrats, Mark!). Deadline is April 13, so hurry up!!!

 

postdoc position in London plus Seattle

Posted in Statistics with tags , , , , , , , , , , , on March 21, 2018 by xi'an

Here is an announcement from Oliver Ratman for a postdoc position at Imperial College London with partners in Seattle, on epidemiology and new Bayesian methods for estimating sources of transmission with phylogenetics. As stressed by Ollie, no pre-requisites in phylogenetics are required, they are really looking for someone with solid foundations in Mathematics/Statistics, especially Bayesian Statistics, and good computing skills (R, github, MCMC, Stan). The search is officially for a Postdoc in Statistics and Pathogen Phylodynamics. Reference number is NS2017189LH. Deadline is April 07, 2018.

more positions in the UK [postdoc & professor]

Posted in Statistics with tags , , , , , , , , , , , on October 13, 2017 by xi'an

I have received additional emails from England advertising for positions in Bristol, Durham, and London, so here they are, with links to the complete advertising!

  1. The University of Bristol is seeking to appoint a number of Chairs in any areas of Mathematics or Statistical Science, in support of a major strategic expansion of the School of Mathematics. Deadline is December 4.
  2. Durham University is opening a newly created position of Professor of Statistics, with research and teaching duties. Deadline is November 6.
  3. Oliver Ratman, in the Department of Mathematics at Imperial College London, is seeking a Research Associate in Statistics and Pathogen Phylodynamics. Deadline is October 30.

Suffrage Science awards in maths and computing

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , on October 21, 2016 by xi'an

On October 11, at Bletchley Park, the Suffrage Science awards in mathematics and computer sciences were awarded for the first time to 12 senior female researchers. Among whom three statisticians, Professor Christl Donnelly from Imperial College London, my colleague at Warwick, Jane Hutton, and my friend and co-author, Sylvia Richardson, from MRC, Cambridge University. This initiative was started by the Medical Research Council in 2011 by Suffrage Science awards for life sciences, followed in 2013 by one for engineering and physics, and this year for maths and computing. The name of the award aims to connect with the Suffragette movement of the late 19th and early 20th Centuries, which were particularly active in Britain. One peculiar aspect of this award is that the recipients are given pieces of jewellery, created for each field, pieces that they will themselves give two years later to a new recipient of their choice, and so on in an infinite regress! (Which suggests a related puzzle, namely to figure out how many years it should take until all female scientists have received the award. But since the number increases as the square of the number of years, this is not going to happen unless the field proves particularly hostile to women scientists!) This jewellery award also relates to the history of the Suffragette movement since the WPSU commissioned their own jewellery awards. A clever additional touch was that the awards were delivered on Ada Lovelace Day, October 11.

inflation, evidence and falsifiability

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , on July 27, 2015 by xi'an

[Ewan Cameron pointed this paper to me and blogged about his impressions a few weeks ago. And then Peter Coles wrote a (properly) critical blog entry yesterday. Here are my quick impressions, as an add-on.]

“As the cosmological data continues to improve with its inevitable twists, it has become evident that whatever the observations turn out to be they will be lauded as \proof of inflation”.” G. Gubitosi et al.

In an arXive with the above title, Gubitosi et al. embark upon a generic and critical [and astrostatistical] evaluation of Bayesian evidence and the Bayesian paradigm. Perfect topic and material for another blog post!

“Part of the problem stems from the widespread use of the concept of Bayesian evidence and the Bayes factor (…) The limitations of the existing formalism emerge, however, as soon as we insist on falsifiability as a pre-requisite for a scientific theory (….) the concept is more suited to playing the lottery than to enforcing falsifiability: winning is more important than being predictive.” G. Gubitosi et al.

It is somehow quite hard not to quote most of the paper, because prose such as the above abounds. Now, compared with standards, the authors introduce an higher level than models, called paradigms, as collections of models. (I wonder what is the next level, monads? universes? paradises?) Each paradigm is associated with a marginal likelihood, obtained by integrating over models and model parameters. Which is also the evidence of or for the paradigm. And then, assuming a prior on the paradigms, one can compute the posterior over the paradigms… What is the novelty, then, that “forces” falsifiability upon Bayesian testing (or the reverse)?!

“However, science is not about playing the lottery and winning, but falsifiability instead, that is, about winning given that you have bore the full brunt of potential loss, by taking full chances of not winning a priori. This is not well incorporated into the Bayesian evidence because the framework is designed for other ends, those of model selection rather than paradigm evaluation.” G. Gubitosi et al.

The paper starts by a criticism of the Bayes factor in the point null test of a Gaussian mean, as overly penalising the null against the alternative being only a power law. Not much new there, it is well known that the Bayes factor does not converge at the same speed under the null and under the alternative… The first proposal of those authors is to consider the distribution of the marginal likelihood of the null model under the [or a] prior predictive encompassing both hypotheses or only the alternative [there is a lack of precision at this stage of the paper], in order to calibrate the observed value against the expected. What is the connection with falsifiability? The notion that, under the prior predictive, most of the mass is on very low values of the evidence, leading to concluding against the null. If replacing the null with the alternative marginal likelihood, its mass then becomes concentrated on the largest values of the evidence, which is translated as an unfalsifiable theory. In simpler terms, it means you can never prove a mean θ is different from zero. Not a tremendously item of news, all things considered…

“…we can measure the predictivity of a model (or paradigm) by examining the distribution of the Bayesian evidence assuming uniformly distributed data.” G. Gubitosi et al.

The alternative is to define a tail probability for the evidence, i.e. the probability to be below an arbitrarily set bound. What remains unclear to me in this notion is the definition of a prior on the data, as it seems to be model dependent, hence prohibits comparison between models since this would involve incompatible priors. The paper goes further into that direction by penalising models according to their predictability, P, as exp{-(1-P²)/P²}. And paradigms as well.

“(…) theoretical matters may end up being far more relevant than any probabilistic issues, of whatever nature. The fact that inflation is not an unavoidable part of any quantum gravity framework may prove to be its greatest undoing.” G. Gubitosi et al.

Establishing a principled way to weight models would certainly be a major step in the validation of posterior probabilities as a quantitative tool for Bayesian inference, as hinted at in my 1993 paper on the Lindley-Jeffreys paradox, but I do not see such a principle emerging from the paper. Not only because of the arbitrariness in constructing both the predictivity and the associated prior weight, but also because of the impossibility to define a joint predictive, that is a predictive across models, without including the weights of those models. This makes the prior probabilities appearing on “both sides” of the defining equation… (And I will not mention the issues of constructing a prior distribution of a Bayes factor that are related to Aitkin‘s integrated likelihood. And won’t obviously try to enter the cosmological debate about inflation.)

recents advances in Monte Carlo Methods

Posted in R, Statistics, Travel, University life with tags , , , , , , , , , , , on February 8, 2012 by xi'an

Next Thursday (Feb. 16), at the RSS, there will be a special half-day meeting (afternoon, starting at 13:30) on Recent Advances in Monte Carlo Methods organised by the General Application Section. The speakers are

  • Richard Everitt, University of Oxford, Missing data, and what to do about it
  • Anthony Lee, Warwick University, Auxiliary variables and many-core computation
  • Nicolas Kantas, Imperial College London, Particle methods for computing optimal control inputs
  • Nick Whitely, Bristol University, Stability properties of some particle filters
  • Simon Maskell, QinetiQ & Imperial College London, Using a Probabilistic Hypothesis Density filter to confirm tracks in a multi-target environment

(Note this is not a Read Paper meeting, so there is no paper nor discussion!)