**A**s it rarely happens with Nature, I just read an article that directly relates to my research interests, about a secure physical random number generator (RNG). By Peter Bierhost and co-authors, mostly physicists apparently. Security here means that the outcome of the RNG is unpredictable. This very peculiar RNG is based on two correlated photons sent to two measuring stations, separated by at least 187m, which have to display unpredictable outcomes in order to respect the impossibility of faster-than-light communications, otherwise known as Bell inequalities. This is hardly practical though, especially when mentioning that the authors managed to produce 2¹⁰ random bits over 10 minutes, post processing “the measurement of 55 million photon pairs”. (I however fail to see why the two-arm apparatus would be needed for regular random generation as it seems relevant solely for the demonstration of randomness.) I also checked the associated supplementary material, which is mostly about proving some total variation bound, and constructing a Bell function. What is most puzzling in this paper (and the associated supplementary material) is the (apparent) lack of guarantee of uniformity of the RNG. For instance, a sentence (Supplementary Material, p.11) about a distribution being “within TV distance of uniform” hints at the method being not provably uniform, which makes the whole exercise incomprehensible…

## Archive for uniformity test

## certified randomness, 187m away…

Posted in Statistics with tags Bell inequality, Nature, quantum computers, random number generation, randomness, RNG, total variation, uniformity test on May 3, 2018 by xi'an## checking ABC convergence via coverage

Posted in pictures, Statistics, Travel, University life with tags ABC, arXiv, Bayesian calibration, confidence sets, credible intervals, DIYABC, p-values, uniformity test on January 24, 2013 by xi'an**D**ennis Prangle, Michael Blum, G. Popovic and Scott Sisson just arXived a paper on diagnostics for ABC validation via coverage diagnostics. Getting valid approximation diagnostics for ABC is clearly and badly needed and this was the last slide of my talk yesterday at the Winter Workshop in Gainesville. When simulation time is not an issue (!), our DIYABC software does implement a limited coverage assessment by computing the type I error, i.e. by simulating data under the null model and evaluating the number of time it is rejected at the 5% level (see sections 2.11.3 and 3.8 in the documentation). The current paper builds on a similar perspective.

**T**he idea in the paper is that a (Bayesian) credible interval at a given credible level α should have a similar confidence level (at least asymptotically and even more for matching priors) and that simulating pseudo-data with a known parameter value allows for a Monte-Carlo evaluation of the credible interval “true” coverage, hence for a calibration of the tolerance. The delicate issue is about the generation of those “known” parameters. For instance, if the pair (θ_{,} y) is generated from the joint distribution prior x likelihood, and if the credible region is also based on the true posterior, the average coverage is the nominal one. On the other hand, if the credible interval is based on a poor (ABC) approximation to the posterior, the average coverage should differ from the nominal one. Given that ABC is *always* wrong, however, this may fail to be a powerful diagnostic. In particular, when using *insufficient* (summary) statistics, the discrepancy should make testing for uniformity harder, shouldn’t it? Continue reading