**W**hile in Cambridge last month, I picked a few books from a local bookstore as fodder for my incoming vacations. Including this omnibus volume made of the first three books by Philip Kerr featuring Bernie Gunther, a private and Reich detective in Nazi Germany, namely, *March Violets* (1989), *The Pale Criminal* (1990), and *A German Requiem* (1991). (Book that I actually read before the vacations!) The stories take place before the war, in 1938, and right after, in 1946, in Berlin and Vienna. The books centre on a German version of Philip Marlowe, wise cracks included, with various degrees of success. (There actually is a silly comparison with Chandler on the back of the book! And I found somewhere else a similarly inappropriate comparison with Graham Greene‘s The Third Man…) Although I read the whole three books in a single week, which clearly shows some undeniable addictive quality in the plots, I find those plots somewhat shallow and contrived, especially the second one revolving around a serial killer of young girls that aims at blaming Jews for those crimes and at justifying further Nazi persecutions. Or the time spent in Dachau by Bernie Gunther as undercover agent for Heydrich. If anything, the third volume taking place in post-war Berlin and Wien is much better at recreating the murky atmosphere of those cities under Allied occupations. But overall there is much too much info-dump passages in those novels to make them a good read. The author has clearly done his documentation job correctly, from the early homosexual persecutions to Kristallnacht, to the fights for control between the occupying forces, but the information about the historical context is not always delivered in the most fluent way. And having the main character working under Heydrich, then joining the SS, does make relating to him rather unlikely, to say the least. It is hence unclear to me why those books are so popular, apart from the easy marketing line that stories involving Nazis are more likely to sell… Nothing to be compared with the fantastic Alone in Berlin, depicting the somewhat senseless resistance of a Berliner during the Nazi years, dropping hand-written messages against the regime under strangers’ doors.

## Archive for Berlin

## Berlin [and Vienna] noir [book review]

Posted in Statistics with tags Alone in Berlin, Berlin, Berlin noir, book reviews, Dachau, Graham Greene, Nazi State, Raymond Chandler, Reinhart Heydrich, Wien, WW II on August 17, 2017 by xi'an## seeking the error in nested sampling

Posted in pictures, Statistics, Travel with tags Berlin, curse of dimensionality, error assessment, John Skilling, Monte Carlo error, nested sampling, Nicolas Chopin on April 13, 2017 by xi'an**A** newly arXived paper on the error in nested sampling, written by Higson and co-authors, and read in Berlin, looks at the difficult task of evaluating the sampling error of nested sampling. The conclusion is essentially negative in that the authors recommend multiple runs of the method to assess the magnitude of the variability of the output by bootstrap, i.e. to call for the most empirical approach…

The core of this difficulty lies in the half-plug-in, half-quadrature, half-Monte Carlo (!) feature of the nested sampling algorithm, in that (i) the truncation of the unit interval is based on a expectation of the mass of each shell (i.e., the zone between two consecutive isoclines of the likelihood, (ii) the evidence estimator is a quadrature formula, and (iii) the level of the likelihood at the truncation is replaced with a simulated value that is not even unbiased (and correlated with the previous value in the case of an MCMC implementation). As discussed in our paper with Nicolas, the error in the evidence approximation is of the same order as other Monte Carlo methods in that it gets down like the square root of the number of terms at each iteration. Contrary to earlier intuitions that focussed on the error due to the quadrature.

But the situation is much less understood when the resulting sample is used for estimation of quantities related with the posterior distribution. With no clear approach to assess and even less correct the resulting error, since it is not solely a Monte Carlo error. As noted by the authors, the quadrature approximation to the univariate integral replaces the unknown prior weight of a shell with its Beta order statistic expectation *and* the average of the likelihood over the shell with a single (uniform???) realisation. Or the mean value of a transform of the parameter with a single (biased) realisation. Since most posterior expectations can be represented as integrals over likelihood levels of the average value over an iso-likelihood contour. The approach advocated in the paper involved multiple threads of an “unwoven nested sampling run”, which means launching n nested sampling runs with one living term from the n currents living points in the current nested sample. (Those threads may then later be recombined into a single nested sample.) This is the starting point to a nested flavour of bootstrapping, where threads are sampled with replacement, from which confidence intervals and error estimates can be constructed. (The original notion appears in Skilling’s 2006 paper, but I missed it.)

The above graphic is an attempt within the paper at representing the (marginal) posterior of a transform f(θ). That I do not fully understand… The notations are rather horrendous as X is not the data but the prior probability for the likelihood to be above a given bound which is actually the corresponding quantile. (There is no symbol for data and £ is used for the likelihood function as well as realisations of the likelihood function…) A vertical slice on the central panel gives the posterior distribution of f(θ) given the event that the likelihood is in the corresponding upper tail. Or given the corresponding shell (?).

## oxwasp@amazon.de

Posted in Books, Kids, pictures, Running, Statistics, Travel, University life with tags Amazon, Berlin, bier, Brauhaus Lemke, doubly intractable problems, Germany, Google, Ising model, machine learning, normalising constant, optimisation, OxWaSP, quantum computers, Spree, Stadtmitte, University of Oxford, University of Warwick, workshop on April 12, 2017 by xi'an**T**he reason for my short visit to Berlin last week was an OxWaSP (Oxford and Warwick Statistics Program) workshop hosted by Amazon Berlin with talks between statistics and machine learning, plus posters from our second year students. While the workshop was quite intense, I enjoyed very much the atmosphere and the variety of talks there. (Just sorry that I left too early to enjoy the social programme at a local brewery, Brauhaus Lemke, and the natural history museum. But still managed nice runs east and west!) One thing I found most interesting (if obvious in retrospect) was the different focus of academic and production talks, where the later do not aim at a full generality or at a guaranteed improvement over the existing, provided the new methodology provides a gain in efficiency over the existing.

This connected nicely with me reading several Nature articles on quantum computing during that trip, where researchers from Google predict commercial products appearing in the coming five years, even though the technology is far from perfect and the outcome qubit error prone. Among the examples they provided, quantum simulation (not meaning what I consider to be *simulation*!), quantum optimisation (as a way to overcome multimodality), and quantum sampling (targeting given probability distributions). I find the inclusion of the latest puzzling in that simulation (in that sense) shows very little tolerance for errors, especially systematic bias. It may be that specific quantum architectures can be designed for specific probability distributions, just like some are already conceived for optimisation. (It may even be the case that quantum solutions are (just next to) available for intractable constants as in Ising or Potts models!)

## Statlearn17, Lyon

Posted in Kids, pictures, R, Statistics, Travel, University life with tags Berlin, conference, France, French Alps, Lyon, machine learning, R, SFDS, Statlearn 2017, train, Université Lumière Lyon 2 on April 6, 2017 by xi'an**T**oday and tomorrow, I am attending the Statlearn17 conference in Lyon, France. Which is a workshop with one-hour talks on statistics and machine learning. And which makes for the second workshop on machine learning in two weeks! Yesterday there were two tutorials in R, but I only took the train to Lyon this morning: it will be a pleasant opportunity to run tomorrow through a city I have not truly ever visited, if X’ed so many times driving to the Alps. Interestingly, the trip started in Paris with me sitting in the train next to another speaker at the conference, despite having switched seat and carriage with another passenger! Speaker whom I did not know beforehand and could only identify him by his running R codes at 300km/h.