## dropping a point

Posted in Statistics, University life with tags , , , , , , , , on September 8, 2020 by xi'an

“A discussion about whether to drop the initial point came up in the plenary tutorial of Fred Hickernell at MCQMC 2020 about QMCPy software for QMC. The issue has been discussed by the pytorch community , and the scipy community, which are both incorporating QMC methods.”

Art Owen recently arXived a paper entitled On dropping the first Sobol’ point in which he examines the impact of a common practice consisting in skipping the first point of a Sobol’ sequence when using quasi-Monte Carlo. By analogy with the burn-in practice for MCMC that aims at eliminating the biais due to the choice of the starting value. Art’s paper shows that by skipping just this one point the rate of convergence of some QMC estimates may drop by a factor, bringing the rate back to Monte Carlo values! As this applies to randomised scrambled Sobol sequences, this is quite amazing. The explanation centers on the suppression leaving one region of the hypercube unexplored, with an O(n⁻¹) error ensuing.

The above picture from the paper makes the case in a most obvious way: the mean squared error is not decreasing at the same rate for the no-drop and one-drop versions, since they are -3/2 and -1, respectively. The paper further “recommends against using roundnumber sample sizes and thinning QMC points.” Conclusion: QMC is not MC!

## Sobol’s Monte Carlo

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , on December 10, 2016 by xi'an

The name of Ilya Sobol is familiar to researchers in quasi-Monte Carlo methods for his Sobol’s sequences. I was thus surprised to find in my office a small book entitled The Monte Carlo Method by this author, which is a translation of his 1968 book in Russian. I have no idea how it reached my office and I went to check with the library of Paris-Dauphine around the corner [of my corridor] whether it had been lost: apparently, the library got rid of it among a collection of old books… Now, having read through this 67 pages book (or booklet as Sobol puts it) makes me somewhat agree with the librarians, in that there is nothing of major relevance in this short introduction. It is quite interesting to go through the book and see the basics of simulation principles and Monte Carlo techniques unfolding, from the inverse cdf principle [established by a rather convoluted proof] to importance sampling, but the amount of information is about equivalent to the Wikipedia entry on the topic. From an historical perspective, it is also captivating to see the efforts to connect physical random generators (such as those based on vacuum tube noise) to shift-register pseudo-random generators created by Sobol in 1958. On a Soviet Strela computer.

While Googling the title of that book could not provide any connection, I found out that a 1994 version had been published under the title of A Primer for the Monte Carlo Method, which is mostly the same as my version, except for a few additional sections on pseudo-random generation, from the congruential method (with a FORTRAN code) to the accept-reject method being then called von Neumann’s instead of Neyman’s, to the notion of constructive dimension of a simulation technique, which amounts to demarginalisation, to quasi-Monte Carlo [for three pages]. A funny side note is that the author notes in the preface that the first translation [now in my office] was published without his permission!