Archive for Wales

and it only gets worse [verbatim]

Posted in Kids, pictures, Travel with tags , , , , , , , , , , , , , , , , , on July 9, 2019 by xi'an

“Increasing export capacity from the Freeport LNG project is critical to spreading freedom gas throughout the world by giving America’s allies a diverse and affordable source of clean energy” M. Menezes, US Secretary of Energy

“NASA should NOT be talking about going to the Moon – We did that 50 years ago. They should be focused on the much bigger things we are doing, including Mars (of which the Moon is a part)” DT,, 7 June

“I just met with the Queen of England (U.K.) [sic], the Prince of Whales [re-sic]” DT, 13 June

“[Sarah Sanders] is going to be leaving the service of her country and she’s going to be going  (…) She’s a very special person, a very, very fine woman, she has been so great, she has such heart, she’s strong but with great, great heart, and I want to thank you for an outstanding job.” DT, 13 June

“…when I asked, ‘How many will die?’ ‘150 people, sir’, was the answer from a General. 10 minutes before the strike I stopped it, not … proportionate to shooting down an unmanned drone.” DT, 21 June

“The reason we have tragedies like that on the border is because that father didn’t wait to go through the asylum process in the legal fashion and decided to cross the river and not only died but his daughter died tragically as well,” K. Cuccinelli, head of US Immigration and Citizenship Services, 28 June

“If Japan is attacked, we will fight World War III. But if we’re attacked, Japan doesn’t have to help us at all. They can watch it on a Sony television.” DT, 24 June

free and graphic session at RSS 2018 in Cardiff

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , on July 11, 2018 by xi'an

Reposting an email I received from the Royal Statistical Society, this is to announce a discussion session on three papers on Data visualization in Cardiff City Hall next September 5, as a free part of the RSS annual conference. (But the conference team must be told in advance.)

Paper:             ‘Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view

Authors:         Stefano Castruccio (University of Notre Dame, USA) and Marc G. Genton and Ying Sun (King Abdullah University of Science and Technology, Thuwal)

 Paper:             Visualization in Bayesian workflow’

Authors:            Jonah Gabry (Columbia University, New York), Daniel Simpson (University of Toronto), Aki Vehtari (Aalto University, Espoo), Michael Betancourt (Columbia University, New York, and Symplectomorphic, New York) and Andrew Gelman (Columbia University, New York)

Paper:             ‘Graphics for uncertainty’

Authors:         Adrian W. Bowman (University of Glasgow)

PDFs and supplementary files of these papers from StatsLife and the RSS website. As usual, contributions can be sent in writing, with a deadline of September 19.

bitcoin and cryptography for statistical inference and AI

Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , on April 16, 2018 by xi'an

A recent news editorial in Nature (15 March issue) reminded me of the lectures Louis Aslett gave at the Gregynog Statistical Conference last week, on the advanced use of cryptography tools to analyse sensitive and private data. Lectures that reminded me of a graduate course I took on cryptography and coding, in Paris 6, and which led me to visit a lab at the Université de Limoges during my conscripted year in the French Navy. With no research outcome. Now, the notion of using encrypted data towards statistical analysis is fascinating in that it may allow for efficient inference and personal data protection at the same time. As opposed to earlier solutions of anonymisation that introduced noise and data degradation, not always providing sufficient protection of privacy. Encryption that is also the notion at the basis of the Nature editorial. An issue completely missing from the paper, while stressed by Louis, is that this encryption (like Bitcoin) is costly, in order to deter hacking, and hence energy inefficient. Or limiting the amount of data that can be used in such studies, which would turn the idea into a stillborn notion.

X entropy for optimisation

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on March 29, 2018 by xi'an

At Gregynog, with mounds of snow still visible in the surrounding hills, not to be confused with the many sheep dotting the fields(!), Owen Jones gave a three hour lecture on simulation for optimisation, which is a less travelled path when compared with simulation for integration. His second lecture covered cross entropy for optimisation purposes. (I had forgotten that Reuven Rubinstein and Dirk Kroese had put forward this aspect of their technique in the very title of their book. As “A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning”.) The X entropy approaches pushes for simulations restricted to top values of the target function, iterating to find the best parameter in the parametric family used for the simulation. (Best to be understood in the Kullback sense.) Now, this is a wee bit like simulated annealing, where lots of artificial entities have to be calibrated in the algorithm, due to the original problem being unrelated to an specific stochastic framework. X entropy facilitates concentration on the highest values of the target, but requires a family of probability distributions that puts weight on the top region. This may be a damning issue in large dimensions. Owen illustrated the approach in the case of the travelling salesman problem, where the parameterised distribution is a Markov chain on the state space of city sequences. Further, if the optimal value of the target is unknown, avoiding getting stuck in a local optimum may be tricky. (Owen presented a proof of convergence for a temperature going to zero slowly enough that is equivalent to a sure exploration of the entire state space, in a discrete setting, which does not provide a reassurance in this respect, as the corresponding algorithm cannot be implemented.) This method falls into the range of methods that are doubly stochastic in that they rely on Monte Carlo approximations at each iteration of the exploration algorithm.

During a later talk, I tried to recycle one of my earlier R codes on simulated annealing for sudokus, but could not find a useful family of proposal distributions to reach the (unique) solution. Using a mere product of distributions on each of the free positions in the sudoku grid only led me to a penalty of 13 errors…

1    2    8    5    9    7    4    9    3
7    3    5    1    2    4    6    2    8
4    6    9    6    3    8    5    7    1
2    7    5    3    1    6    9    4    8
8    1    4    7    8    9    7    6    2
6    9    3    8    4    2    1    3    5
3    8    6    4    7    5    2    1    9
1    4    2    9    6    3    8    5    7
9    5    7    2    1    8    3    4    6

It is hard to consider a distribution on the space of permutations, 𝔖⁸¹.

Gregynog Hall ciplun [jatp]

Posted in Mountains, pictures, Running, Travel, University life with tags , , , , on March 25, 2018 by xi'an

back to Wales [54th Gregynog Statistical Conference]

Posted in Mountains, pictures, Running, Travel, University life with tags , , , , , , , , on March 23, 2018 by xi'an

Today, provided the Air France strike let me fly to Birmingham airport!, I am back at Gregynog Hall, Wales, for the weekend conference organised there every year by some Welsh and English statistics departments, including Warwick. Looking forward to the relaxed gathering in the glorious Welsh countryside (and hoping that my knee will have sufficiently recovered for some trail running around Gregynog Hall…!) Here are the slides of the talk I will present tomorrow:

the invasion of the stochastic gradients

Posted in Statistics with tags , , , , , , , , , on May 10, 2017 by xi'an

Within the same day, I spotted three submissions to arXiv involving stochastic gradient descent, that I briefly browsed on my trip back from Wales:

  1. Stochastic Gradient Descent as Approximate Bayesian inference, by Mandt, Hoffman, and Blei, where this technique is used as a type of variational Bayes method, where the minimum Kullback-Leibler distance to the true posterior can be achieved. Rephrasing the [scalable] MCMC algorithm of Welling and Teh (2011) as such an approximation.
  2. Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent, by Arnak Dalalyan, which establishes a convergence of the uncorrected Langevin algorithm to the right target distribution in the sense of the Wasserstein distance. (Uncorrected in the sense that there is no Metropolis step, meaning this is a Euler approximation.) With an extension to the noisy version, when the gradient is approximated eg by subsampling. The connection with stochastic gradient descent is thus tenuous, but Arnak explains the somewhat disappointing rate of convergence as being in agreement with optimisation rates.
  3. Stein variational adaptive importance sampling, by Jun Han and Qiang Liu, which relates to our population Monte Carlo algorithm, but as a non-parametric version, using RKHS to represent the transforms of the particles at each iteration. The sampling method follows two threads of particles, one that is used to estimate the transform by a stochastic gradient update, and another one that is used for estimation purposes as in a regular population Monte Carlo approach. Deconstructing into those threads allows for conditional independence that makes convergence easier to establish. (A problem we also hit when working on the AMIS algorithm.)