Archive for the Travel Category

tramonto a Venezia [jatp]

Posted in pictures, Travel with tags , , , , , , , on October 27, 2016 by xi'an

a Venezia [ESOBE 2016]

Posted in pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , on October 26, 2016 by xi'an

Piazza Venezia, Roma, March 01, 2012Tomorrow I am off to Venezia for three days, attending the ESOBE 2016 workshop, where ESOBE stands for European Seminar on Bayesian Econometrics. This year it is indeed taking place in Venezia, Università Ca’ Foscari, in this beautiful building on the Gran Canale, and I have been invited to give a talk. Excited to get back to this unique place, hoping the high water will not be too high to prevent getting around (at random as usual).

art brut

Posted in pictures, Running, Travel with tags , , , , , , , , , on October 22, 2016 by xi'an


Posted in Kids, Travel, Wines with tags , , , on October 20, 2016 by xi'an

scalable Langevin exact algorithm

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , on October 18, 2016 by xi'an

“By employing a modification to existing naïve subsampling techniques we can obtain an algorithm which is still exact but has sub-linear iterative cost as a function of data size.”

A few weeks ago Murray Pollock, Paul Fearnhead, Adam Johansen and Gareth Roberts (all from Warwick except for Paul) arXived a paper The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data. (This was also the topic of Murray’s talk last year at JSM in Seattle.) One major advance found in the paper is the derivation of an “exact” algorithm that is sub-linear in the data size. As discussed in the introduction, the current approaches to large data problems either suffer from being approximate (like divide-and-conquer methods) or do not achieve significant reduction in the computing time, being of order O(n). The authors mention Teh and Welling (2011) sand their tochastic gradient approximation to the Langevin diffusion, when the gradient is based on a subsample. Without the Metropolis correction that would ensure an exact target but at a cost of order O(n). (Which makes the technique rather difficult to recommend.)

A novel [for me] notion at the core of this paper is the concept of quasi-stationary distribution, which is the limiting distribution of a Markov chain X[t] conditional on a Markov stopping time [being larger than t]. The approach is based on diffusions with appropriate stationary distributions like the Langevin diffusion. (Actually, as in most papers I have read and remember, the current paper only considers the Langevin diffusion.) In order to avoid the issues with unadjusted and Metropolis-adjusted Langevin schemes, a killed Brownian motion is created, which means a Brownian motion conditional of being alive till time T when the instantaneous killing rate is a given function of the chain, Φ(X[t]), related with the stationary measure of the Langevin diffusion ν. Under appropriate conditions, the density of this killed Brownian motion converges [in T] to √ν. Which immediately hints at creating a new Langevin diffusion targeting ν² instead of ν. And killing it with the proper rate, which can be done by thinning a Poisson process. Simulating the original path can be done by path-space rejection sampling, following the technique set by Gareth Roberts and co-authors more than ten years ago. Based on finite dimensional realisations of the path on [0,T]. And including the killing part can be done by importance sampling and checking that the simulated killing time is larger than the current (exponentially simulated) time.

One practical difficulty in the implementation of this neat principle is the derivation of the normalising constant, which evaluation degrades with the time horizon T. The solution adopted in the paper is through a sequential Monte Carlo method, using another discretisation of the time interval [0,T] (relying on the original one would get too costly?). As for subsampling, since the survival probability for the Brownian motion is based on an unbiased estimator, subsampling does not hurt if conducted in a random manner. Although this increases the variance on principle, the use of a control variate computed just once helps in reducing the complexity to O(1).

This is a tough paper and I have not gone through the effort of trying to implement it, but this is an original and innovative construct I would like to monitor in further details on a toy example, maybe next week while in Warwick. Or at least to discuss it with the authors.

Norman sunrise [jatp]

Posted in pictures, Running, Travel with tags , , , , , on October 16, 2016 by xi'an

Only in Britain…

Posted in Kids, pictures, Travel, University life with tags , , , , , on October 15, 2016 by xi'an

A recent announcement on the University of Warwick official website:

Today the Minister for the Constitution, Chris Skidmore, presented WMG, at the University of Warwick, with a Royal Warrant signed by Her Majesty the Queen, officially conferring her Majesty’s recognition with the title of the Regius Professor of Manufacturing (Engineering).

The title of Regius Professorship is a rare and prestigious award given by Her Majesty the Queen to recognise exceptionally high quality research at an institution. The University of Warwick was one of 12 universities honoured to mark Her Majesty’s 90th Birthday. Previous to this, only 14 had been granted since the reign of Queen Victoria. It is believed that the first Regius Professorship was conferred to Aberdeen University in 1497 by King James IV.