## Normal tail precision

In 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)

$m(x_1,\ldots,x_n)=2^{-n/2}\,\sum_{i=0}^n\,e^{\sqrt{2}\sum_{j=1}^i x_j- \sqrt{2}\sum_{j=i+1}^n x_j+(n-2i)^2\sigma^2}$

$\qquad\qquad\times \left[ \Phi(\{x_{i+1}-\sqrt{2}(n-2i)\sigma^2\}/\sigma) - \Phi(\{x_{i}-\sqrt{2}(n-2i)\sigma^2\}/\sigma) \right]$

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)))
}


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

> 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


And 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))
}


Here 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


### One Response to “Normal tail precision”

1. […] my earlier posts on the revision of Lack of confidence, here is an interesting outcome from the derivation of the exact marginal likelihood in the Laplace case. Computing the posterior probability of a normal model versus a Laplace model […]