Given n tokens with 10≤n≤25, Alice and Bob play the following game: the first player draws an integer1≤m≤6 at random. This player can then take 1≤r≤min(2m,n) tokens. The next player is then free to take1≤s≤min(2r,n-r) tokens. The player taking the last tokens is the winner. There is a winning strategy for Alice if she starts with m=3 and if Bob starts with m=2. Deduce the value of n.

Although I first wrote a brute force version of the following code, a moderate amount of thinking leads to conclude that the person given n remaining token and an adversary choice of m tokens such that 2m≥n always win by taking the n remaining tokens:

optim=function(n,m){ outcome=(n<2*m+1) if (n>2*m){ for (i in 1:(2*m)) outcome=max(outcome,1-optim(n-i,i)) } return(outcome) }

eliminating solutions which dividers are not solutions themselves:

sol=lowa=plura[plura<100] for (i in 3:6){ sli=plura[(plura>10^(i-1))&(plura<10^i)] ace=sli-10^(i-1)*(sli%/%10^(i-1)) lowa=sli[apply(outer(ace,lowa,FUN="=="), 1,max)==1] lowa=sort(unique(lowa)) sol=c(sol,lowa)}

which leads to the output

> subs=rep(0,16) > for (n in 10:25) subs[n-9]=optim(n,3) > for (n in 10:25) if (subs[n-9]==1) subs[n-9]=1-optim(n,2) > subs [1] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 > (10:25)[subs==1] [1] 18

Ergo, the number of tokens is 18!

Filed under: Books, Kids, R, Statistics, University life Tagged: Le Monde, mathematical puzzle, R, recursive function ]]>

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As in Chamonix, there will be parallel sessions and hence the scientific committee has issued a call for proposals to organise contributed sessions, tutorials and the presentation of posters on particularly timely and exciting areas of research relevant and of current interest to Bayesian Computation. All proposals should be sent to Mark Girolami directly by May the 4th (be with him!).

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**A** self-study question on X validated exposed an interesting property of the Beta distribution:

If x is B(n,m) and y is B(n+½,m) then √xy is B(2n,2m)

While this can presumably be established by a mere change of variables, I could not carry the derivation till the end and used instead the moment generating function E[(XY)^{s/2}] since it naturally leads to ratios of B(a,b) functions and to nice cancellations thanks to the ½ in some Gamma functions [and this was the solution proposed on X validated]. However, I wonder at a more fundamental derivation of the property that would stem from a statistical reasoning… Trying with the ratio of Gamma random variables did not work. And the connection with order statistics does not apply because of the ½. Any idea?

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The presumably most pessimistic conclusion of this study is that the connections between (i) the true posterior probability of a model, (ii) the ABC version of this probability, and (iii) the random forest version of the above, are at best very loose. This leaves open queries for acceptable approximations of (i), since the posterior predictive error is instead an error assessment for the ABC RF model choice procedure. While a Bayesian quantity that can be computed at little extra cost, it does not necessarily compete with the posterior probability of a model.

reflecting my hope that we can eventually come up with a proper approximation to the “true” posterior probability…

Filed under: Books, pictures, Statistics, University life, Wines Tagged: ABC, ABC model choice, Handbook of Approximate Bayesian computation, likelihood-free methods, Montpellier, PNAS, random forests, survey ]]>

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The argument against considering a limiting value for the posterior probability is that it converges to 0, 21, or an intermediate value. In the first two cases it is useless. In the medium case. achieved when the prior probability of the null and alternative hypotheses depend on variance σ². While I do not want to argue in favour of my 1993 solution

since it is ill-defined in measure theoretic terms, I do not buy the coherence argument that, since this prior probability converges to zero when σ² goes to infinity, the posterior probability should also go to zero. In the limit, probabilistic reasoning fails since the prior under the alternative is a measure not a probability distribution… We should thus abstain from over-interpreting improper priors. (A sin sometimes committed by Jeffreys himself in his book!)

Filed under: Books, Kids, Statistics Tagged: Bayesian tests of hypotheses, Capitaine Haddock, Dennis Lindley, Harold Jeffreys, improper priors, Jeffreys-Lindley paradox, model posterior probabilities, Tintin ]]>