## Archive for the R Category

## bayess’ back! [on CRAN]

Posted in Books, R, Statistics, University life with tags Bayesian Essentials with R, Bayesian textbook, CRAN, package, R, update, verion 1.5, version on September 22, 2022 by xi'an## why is this algorithm simulating a Normal variate?

Posted in Books, Kids, R, Statistics with tags cross validated, Devroye, Introducing Monte Carlo Methods with R, Luc Devroye, Monte Carlo Statistical Methods, Non-Uniform Random Variate Generation, normal generator, simulation 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
```

## fast track & slow lane

Posted in Books, Kids, R, Statistics with tags grandes écoles, oral exam, order statistics, riddle, The Riddler on September 3, 2022 by xi'an**A** riddle from the Riddler where N cars going at random (iid) speeds drive a road with a slow and a fast lane, each car choosing the fast lane iff any of the cars ahead in the slow lane is slowerthan them. With the question of the average number of car convoys.

If there were one single lane, the problem would be to determine how many times a smaller realisation and it has been solved in a much older Riddler. Namely

In the two-lane system, the slow one only gathers cars with speeds lowest than the current speed, i.e. a decreasing sequence of speeds, creating single car convoys. The fast lane thus captures cars that are above the current minimum in the sequence, which, as it converges to the global minimum at some points, means that all following cars are found in the fast lane. i thought this would bring the number of convoys close to twice the above logarithmic sum (which sealed my destiny during an entrance oral exam 40 years ago!), but there are actually more of them for N large enough , which may be due to the possibility of the slow lane to capture more moderate speed cars in the beginning… The above compares the number of convoys for one lane (in red) and two (in gold), as well as the remarkable fit when regressing these numbers against log(N).

Here is the R code I used for that simulation

convoy=function(N,T=1e5){ for(t in 1:T){ speed=runif(N) slow=fast=NULL slow=speed[1] for(i in 2:N){ if(speed[i]<min(slow))slow=c(slow,speed[i]) else fast=c(fast,speed[i])} F=F+length(slow) if(length(fast)>0)F=F+1+sum(fast[-1]<cummin(fast)[-length(fast)])} return(F/T)}

## optimal Gaussian zorbing

Posted in Books, Kids, R, Statistics with tags FiveThirtyEight, non-central chi-square distribution, puzzle, R, simulated annealing, The Riddler, zorbing on August 30, 2022 by xi'an**A** zorbing puzzle from the Riddler: *cover the plane with four non-intersecting disks of radius one towards getting the highest probability (under the standard bivariate Normal distribution)*.

As I could not see a simple connection between the disks and the standard Normal, beyond the probability of a disk being given by a non-central chi-square cdf (with two degrees of freedom), I (once again) tried a random search by simulated annealing, which ended up with a configuration like the above, never above 0.777 using a pedestrian R code like

for(t in 1:1e6){# move the disk centres Ap=A+vemp*rnorm(2) Bp=B+vemp*rnorm(2) while(dist(rbind(Ap,Bp))<2)Bp=B+vemp*rnorm(2) Cp=C+vemp*rnorm(2) while(min(dist(rbind(Ap,Bp,Cp)))<2)Cp=C+vemp*rnorm(2) Dp=D+vemp*rnorm(2) while(min(dist(rbind(Ap,Bp,Cp,Dp)))<2)Dp=D+vemp*rnorm(2) #coverage probability pp=pchisq(1,df=2,ncp=Ap%*%Ap)+pchisq(1,df=2,ncp=Bp%*%Bp)+ pchisq(1,df=2,ncp=Cp%*%Cp)+pchisq(1,df=2,ncp=Dp%*%Dp) #simulated annealing step if(log(runif(1))<(pp-p)/sqrt(temp)){ A=Bp;B=Cp;C=Dp;D=Ap;p=pp if (sol$val<p) sol=list(val=pp,pos=rbind(A,B,C,D))} temp=temp*.9999}

I also tried a simpler configuration where all disk centres were equidistant from a reference centre, but this led to a lower “optimal” probability. I was looking forward the discussion of the puzzle, to discover if anything less brute-force was possible! But there was no deeper argument there beyond the elimination of other “natural” configurations (and missing the non-central χ² connection!). Among these options, having two disks tangent at (0,0) were optimal. But the illustration was much nicer:

## the Kelly criterion and then some

Posted in R, Statistics with tags betting, Kelly criterion, R, riddle, sequential decision theory, The Riddler on August 26, 2022 by xi'anThe Kelly criterion is a way to optimise an unlimited sequence of bets under the following circumstances: a probability *p* of winning each bet, a loss of a fraction *a* of the sum bet, a gain of a fraction *b* of the sum bet, and a fraction *f* of the current fortune as the sum bet. Then

is the fraction optimising the growth

Here is a rendering of the empirical probability of reaching 250 before ruin, when starting with a fortune of 100, when *a=1, p=0.3* and *f* and *b* vary (on a small grid). With on top Kelly’s solutions, meaning that they achieve a high probability of avoiding ruin. Until they cannot.

The Ridder is asking for a variant of this betting scheme, when the probability *p* to win the bet is proportional to *1/(1+b),* namely *.9/(1+b)*. In that case, the probabilities of reaching 250 (using the same R code as above) before ruin are approximated as followswith a maximal probability that does not exceed 0.36, the probability to win in one go, betting 100 with a goal of 250. It thus may be that the optimal choice, probabilitiwise is that one. Note that in that case, whatever the value of *b*, the Kelly criterion returns a negative fraction. Actually, the solution posted by the Riddler the week after is slightly above, 0.3686 or 1−(3/5)^{9/10}. Which I never reached by the sequential bet of a fixed fraction of the current fortune, eps. when not accounting for the fact that aiming at 250 rather than a smaller target was removing a .9 factor.