# Normal tail precision

**Xi'an's Og » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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**I**n conjunction with the normal-Laplace comparison mentioned in the most recent post about our lack of confidence in ABC model choice, we have been working on the derivation of the exact Bayes factor and I derived an easy formula for the marginal likelihood in the Laplace case that boils down to a weighted sum of normal probabilities (with somehow obvious notations)

**I** then wrote a short R code that produced this marginal likelihood.

# ABC model comparison between Laplace and normal # L(mu,V2) versus N(mu,1) with a prior N(0,2*2) nobs=21 nsims=500 sqrtwo=sqrt(2) marnor=function(smpl){ -0.5*nobs*log(2*pi)-0.5*(nobs-1)*var(smpl)+0.5*log(1+1/(4*nobs))-0.5*mean(smpl)^2/(4+1/nobs)} marlap=function(sampl){ smpl=sort(sampl) S=sum(smpl) S=c(S,S-2*cumsum(smpl)) phi=pnorm((smpl-sqrtwo*4*(nobs-2*(1:nobs)))/2) phip=pnorm((smpl-sqrtwo*4*(nobs-2*(1:nobs)+2))/2) Dphi=log(c(phip[1],phip[-1]-phi[-nobs],1-phi[nobs])) -0.5*nobs*log(2)+log(sum(exp(-sqrtwo*S+4*(nobs-2*(0:nobs))^2+Dphi))) }

**W**hen checking it with an alternative Monte Carlo integration, Jean-Michel Marin spotted a slight but persistent numerical difference:

H > test=sample(c(-1,1),nobs,rep=TRUE)*rexp(nobs,sqrt(2)) > exp(marlap(test)) [1] 3.074013e-10 > f=function(x){exp(-sqrt(2)*sum(abs(test-x)))*2^(-nobs/2)} > mean(apply(as.matrix(2*rnorm(10^6)),1,f)) [1] 3.126421e-11

**A**nd while I thought it could be due to the simulation error, he persisted in analysing the problem until he found the reason: the difference between the normal cdfs in the above marginal was replaced by zero in the tails of the sample, while it contributed in a significant manner, due to the huge weights in front of those differences! He then rewrote the marlap function so that the difference was better computed in the tails, with a much higher level of agreement!

marlap=function(test){ sigma2=4 lulu=rep(0,nobs-1) test=sort(test) for (i in 1:(nobs-1)){ cst=sqrt(2)*(nobs-2*i)*sigma2 if (test[i]<0) lulu[i]=exp(sqrt(2)*sum(test[1:i])-sqrt(2)*sum(test[(i+1):nobs])+ (nobs-2*i)^2*sigma2+pnorm((test[i+1]-cst)/sqrt(sigma2),log=TRUE)+ log(1-exp(pnorm((test[i]-cst)/sqrt(sigma2),log=TRUE)- pnorm((test[i+1]-cst)/sqrt(sigma2),log=TRUE)))) else lulu[i]=exp(sqrt(2)*sum(test[1:i])-sqrt(2)*sum(test[(i+1):nobs])+ (nobs-2*i)^2*sigma2+pnorm(-(test[i]-cst)/sqrt(sigma2),log=TRUE)+ log(1-exp(pnorm(-(test[i+1]-cst)/sqrt(sigma2),log=TRUE)- pnorm(-(test[i]-cst)/sqrt(sigma2),log=TRUE)))) if (lulu[i]==0) lulu[i]=exp(sqrt(2)*sum(test[1:i])-sqrt(2)*sum(test[(i+1):nobs])+ (nobs-2*i)^2*sigma2+log(pnorm((test[i+1]-cst)/sqrt(sigma2))- pnorm((test[i]-cst)/sqrt(sigma2)))) } lulu0=exp(-sqrt(2)*sum(test[1:nobs])+nobs^2*sigma2+ pnorm((test[1]-sqrt(2)*nobs*sigma2)/sqrt(sigma2),log=TRUE)) lulun=exp(sqrt(2)*sum(test[1:nobs])+nobs^2*sigma2+ pnorm(-(test[nobs]+sqrt(2)*nobs*sigma2)/sqrt(sigma2),log=TRUE)) 2^(-nobs/2)*sum(c(lulu0,lulu,lulun)) }

**H**ere is an example of this agreement:

> marlap(test) [1] 5.519428e-10 mean(apply(as.matrix(2*rnorm(10^6)),1,f)) [1] 5.540964e-10

Filed under: R, Statistics, University life Tagged: ABC, Bayesian model choice, Laplace distribution, normal tail, pnorm, R

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