For ‘Og’s readers interested in lecturer or professor positions in French universities next academic year, including a lecturer position at Paris-Dauphine in applied and computational statistics!, you need to apply for a qualification label by a national committee which strict deadline is next Tuesday, October 25, at 4pm (Paris/CET time). (The whole procedure is exclusively in French!)
Archive for Université Paris Dauphine
This is the cover page of Marco Banterle‘s thesis, who will defend on Thursday [July 21, 13:00], at a rather quiet time for French universities, which is one reason for advertising it here. The thesis is built around several of Marco’s papers, like delayed acceptance, dimension expansion, and Gaussian copula for graphical models. The defence is open to everyone, so feel free to join if near Paris-Dauphine!
This was a time for thrice celebrating here as, a few weeks ago, my daughter Rachel was admitted to her French medical school after successfully passing the deadly entrance exam, Ingmar Schuster defended his PhD, and Victor Elvira [currently visiting us in Dauphine] got a permanent academic position offer in Telecom-Lille. Congrats!!! [The posting was delayed until the position became official. In the French sense.]
“In this paper we will make conceptually simple generalization of Metropolis algorithm, by adjusting the acceptance ratio formula so that the transition probabilities are unaffected by the fluctuations in the estimate of [the acceptance ratio]…”
Last Friday, in Paris-Dauphine, my PhD student Changye Wu showed me a paper of Ceperley and Dewing entitled the penalty method for random walks with uncertain energies. Of which I was unaware of (and which alas pre-dated a recent advance made by Changye). Despite its physics connections, the paper is actually about estimating a Metropolis-Hastings acceptance ratio and correcting the Metropolis-Hastings move for this estimation. While there is no generic solution to this problem, assuming that the logarithm of the acceptance ratio estimate is Gaussian around the true log acceptance ratio (and hence unbiased) leads to a log-normal correction for the acceptance probability.
“Unfortunately there is a serious complication: the variance needed in the noise penalty is also unknown.”
Even when the Gaussian assumption is acceptable, there is a further issue with this approach, namely that it also depends on an unknown variance term. And replacing it with an estimate induces further bias. So it may be that this method has not met with many followers because of those two penalising factors. Despite precluding the pseudo-marginal approach of Mark Beaumont (2003) by a few years, with the later estimating separately numerator and denominator in the Metropolis-Hastings acceptance ratio. And hence being applicable in a much wider collection of cases. Although I wonder if some generic approaches like path sampling or the exchange algorithm could be applied on a generic basis… [I just realised the title could be confusing in relation with the current football competition!]
Richard Everitt organises an afternoon workshop on Bayesian computation in Reading, UK, on April 19, the day before the Estimating Constant workshop in Warwick, following a successful afternoon last year. Here is the programme:
1230-1315 Antonietta Mira, Università della Svizzera italiana 1315-1345 Ingmar Schuster, Université Paris-Dauphine 1345-1415 Francois-Xavier Briol, University of Warwick 1415-1445 Jack Baker, University of Lancaster 1445-1515 Alexander Mihailov, University of Reading 1515-1545 Coffee break 1545-1630 Arnaud Doucet, University of Oxford 1630-1700 Philip Maybank, University of Reading 1700-1730 Elske van der Vaart, University of Reading 1730-1800 Reham Badawy, Aston University 1815-late Pub and food (SCR, UoR campus)
and the general abstract:
The Bayesian approach to statistical inference has seen major successes in the past twenty years, finding application in many areas of science, engineering, finance and elsewhere. The main drivers of these successes were developments in Monte Carlo methods and the wide availability of desktop computers. More recently, the use of standard Monte Carlo methods has become infeasible due the size and complexity of data now available. This has been countered by the development of next-generation Monte Carlo techniques, which are the topic of this meeting.
The meeting takes place in the Nike Lecture Theatre, Agriculture Building [building number 59].