## why is this algorithm simulating a Normal variate?

Posted in Books, Kids, R, Statistics with tags , , , , , , , on September 15, 2022 by xi'an

A backward question from X validated as to why the above is a valid Normal generator based on exponential generations. Which can be found in most textbooks (if not ours). And in The Bible, albeit as an exercise. The validation proceeds from the (standard) Exponential density dominating the (standard) Normal density and, according to Devroye, may have originated from von Neumann himself. But with a brilliant reverse engineering resolution by W. Huber on X validated. While a neat exercise, it requires on average 2.64 Uniform generations per Normal generation, against a 1/1 ratio for Box-Muller (1958) polar approach, or 1/0.86 for the Marsaglia-Bray (1964) composition-rejection method. The apex of the simulation jungle is however Marsaglia and Tsang (2000) ziggurat algorithm. At least on CPUs since, Note however that “The ziggurat algorithm gives a more efficient method for scalar processors (e.g. old CPUs), while the Box–Muller transform is superior for processors with vector units (e.g. GPUs or modern CPUs)” according to Wikipedia.

To draw a comparison between this Normal generator (that I will consider as von Neumann’s) and the Box-Müller polar generator,

#Box-Müller
bm=function(N){
a=sqrt(-2*log(runif(N/2)))
b=2*pi*runif(N/2)
return(c(a*sin(b),a*cos(b)))
}

#vonNeumann
vn=function(N){
u=-log(runif(2.64*N))
v=-2*log(runif(2.64*N))>(u-1)^2
w=(runif(2.64*N)<.5)-2
return((w*u)[v])
}


here are the relative computing times

> system.time(bm(1e8))
utilisateur     système      écoulé
7.015       0.649       7.674
> system.time(vn(1e8))
utilisateur     système      écoulé
42.483       5.713      48.222


## wrong algebra for slice sampler

Posted in Books, Kids, R, Statistics with tags , , , , , , , , , , , , on January 27, 2021 by xi'an

Once more, and thrice alas!, I became aware of a typo in our “Use R!” book through a question on X validated from a reader unable to reproduce the slice of a basic 2D slice sampler for a logistic regression with coefficients (a,b). Indeed, our slice reads as the incorrect set (missing the i=1,…,n)

$\left\{ (a,b): y_i(a+bx_i) > \log \frac{u_i}{1-u_i} \right\}$

when it should have been

$\bigcap_{i=1} \left\{ (a,b)\,:\ (-1)^{y_i}(a+bx_i) > \log\frac{u_i}{1-u_i} \right\}$

which is the version I found in my LaTeX file. So I do not know what happened (unless I corrected the LaTeX file at a later date and cannot remember it, but the latest chance on the file reads October 2011…). Fortunately, the resulting slices in a and b and the following R code remain correct. Unfortunately, both French and Japanese translations reproduce the mistake…

## out of desperation

Posted in Books, Kids, Statistics, University life with tags , , , on November 9, 2018 by xi'an

Someone desperately seeking solutions to the even numbered questions of Introducing Monte Carlo Methods with R…. How odd!

## Example 7.3: what a mess!

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , on November 13, 2016 by xi'an

A rather obscure question on Metropolis-Hastings algorithms on X Validated ended up being about our first illustration in Introducing Monte Carlo methods with R. And exposing some inconsistencies in the following example… Example 7.2 is based on a [toy] joint Beta x Binomial target, which leads to a basic Gibbs sampler. We thought this was straightforward, but it may confuse readers who think of using Gibbs sampling for posterior simulation as, in this case, there is neither observation nor posterior, but simply a (joint) target in (x,θ).

And then it indeed came out that we had incorrectly written Example 7.3 on the [toy] Normal posterior, using at times a Normal mean prior with a [prior] variance scaled by the sampling variance and at times a Normal mean prior with a [prior] variance unscaled by the sampling variance. I am rather amazed that this did not show up earlier. Although there were already typos listed about that example.

## done! [#2]

Posted in Kids, Statistics, University life with tags , , , , , , , , , on January 21, 2016 by xi'an

Phew! I just finished my enormous pile of homeworks for the computational statistics course… This massive pile is due to an unexpected number of students registering for the Data Science Master at ENSAE and Paris-Dauphine. As I was not aware of this surge, I kept to my practice of asking students to hand back solved exercises from Monte Carlo Statistical Methods at the beginning of each class. And could not change the rules of the game once the course had started! Next year, I’ll make sure to get some backup for grading those exercises. Or go for group projects instead…