Archive for probability

riddles on a line [#2]

Posted in Books, Kids, R with tags , , , , , , , on September 11, 2018 by xi'an

A second Riddle(r), with a puzzle related with the integer set Ð={,12,3,…,N}, in that it summarises as

Given a random walk on Ð, starting at the middle N/2, with both end states being absorbing states, and a uniform random move left or right of the current value to the (integer) middle of the corresponding (left or right) integer interval, what is the average time to one absorbing state as a function of N?

Once the Markov transition matrix M associated with this random walk is defined, the probability of reaching an absorbing state in t steps can be derived from the successive powers of M by looking at the difference between the probabilities to be (already) absorbed at both t-1 and t steps. From which the average can be derived.

avexit <- function(N=100){
 #transition matrix M for the walk
 #1 and N+2 are trapping states
 tranz=matrix(0,N+2,N+2)
 tranz[1,1]=tranz[N+2,N+2]=1
 for (i in 2:(N+1))
  tranz[i,i+max(trunc((N+1-i)/2),1)]=tranz[i,i-max(trunc((i-2)/2),1)]=1/2
 #probabilities of absorption
 prowin=proloz=as.vector(0)
 init=rep(0,N+2)
 init[trunc((N+1)/2)]=1 #first position
 curt=init
 while(1-prowin[length(prowin)]-proloz[length(prowin)]>1e-10){
  curt=curt%*%tranz
  prowin=c(prowin,curt[1])
  proloz=c(proloz,curt[N+2])}
 #probability of new arrival in trapping state
 probz=diff(prowin+proloz)
 return(sum((2:length(proloz))*probz))}

leading to an almost linear connection between N and expected trapping time.

riddles on a line

Posted in Books, Kids, R with tags , , , , , , , , , on August 22, 2018 by xi'an

In the Riddler of August 18, two riddles connected with the integer set Ð={2,3,…,10}:

  1. Given a permutation of Ð, what is the probability that the most likely variation (up or down) occurs at each term?
  2. Given three players choosing three different integers in Ð sequentially, and rewards in Ð allocated to the closest of the three players (with splits in case of equal distance), what is the reward for each given an optimal strategy?

For the first question, a simple code returns 0.17…

winofail<-function(permz){ 
  if (length(permz)>1){
    lenoperm=length(permz[-1])/2
    win=(permz[1]<permz[2])*(sum(permz[-1]>permz[1])>lenoperm)+
     (permz[1]>permz[2])*(sum(permz[-1]<permz[1])>lenoperm)+
      (runif(1)<.5)*(sum(permz[-1]>permz[1])==lenoperm)
    win=win&&winofail(permz[-1])
  }else win=TRUE
  return(win)}

(but the analytic solution exhibits a cool Pascal triangular relation!) and for the second question, a quick recursion or dynamic programming produces 20, 19, 15 as the rewards (and 5, 9, 8 as the locations)

gainz<-function(seqz){
  difz=t(abs(outer(2:10,seqz,"-")))
  cloz=apply(difz,2,rank)
  return(apply(rbind(2:10,2:10,2:10)*
   ((cloz==1)+.5*(cloz==1.5)),1,sum))}

timeline<-function(prev){ 
  if (length(prev)==0){ 
   sol=timeline(10);bez=gainz(sol)[1] 
   for (i in 2:9){ 
    bol=timeline(i);comp=gainz(bol)[1] 
    if (comp>bez){
        bez=comp;sol=bol}}}
  if (length(prev)==1){
    bez=-10
    for (i in (2:10)[-(prev-1)]){
      bol=timeline(c(prev,i))
      comp=gainz(bol)[2]
      if (comp>bez){
        bez=comp;sol=bol}}}
  if (length(prev)==2){
    bez=-10
    for (i in (2:10)[-(prev-1)]){
      bol=c(prev,i)
      comp=gainz(bol)[3]
      if (comp>bez){
        bez=comp;sol=bol}}}
  return(sol)}

After reading the solution on the Riddler, I realised I had misunderstood the line as starting at 2 when it was actually starting from 1. Correcting for this leads to the same 5, 9, 8 locations of picks, with rewards of 21, 19, 15.

Why is it necessary to sample from the posterior distribution if we already KNOW the posterior distribution?

Posted in Statistics with tags , , , , , , , , on October 27, 2017 by xi'an

I found this question on X validated somewhat hilarious, the more because of the shouted KNOW! And the confused impression that because one can write down π(θ|x) up to a constant, one KNOWS this distribution… It is actually one of the paradoxes of simulation that, from a mathematical perspective, once π(θ|x) is available as a function of (θ,x), all other quantities related with this distribution are mathematically perfectly and uniquely defined. From a numerical perspective, this does not help. Actually, when starting my MCMC course at ENSAE a few days later, I had the same question from a student who thought facing a density function like

f(x) ∞ exp{-||x||²-||x||⁴-||x||⁶}

was enough to immediately produce simulations from this distribution. (I also used this example to show the degeneracy of accept-reject as the dimension d of x increases, using for instance a Gamma proposal on y=||x||. The acceptance probability plunges to zero with d, with 9 acceptances out of 10⁷ for d=20.)