#
# Code for implementing the Toni et al. alogrithm with predefined levels of
# approximation.
#
# Implementation for the Abakiliki data
tstartC=proc.time()
#
#
# Data
N=120
mstar=30
# Predefined thresholds.
epsil=c(10,5,2,1,0)
epsil=c(10,0)
TT=length(epsil)
# Estimate of posterior mean and variance at each threshold.
EstMean=rep(0,TT)
EstSD=rep(0,TT)
# Total number of simulations
SIMtotal=rep(0,TT)
# Number of particles.
run=10000
output=ABCrej(N,mstar,epsil[1],run)
samp=output$samp
simTotal=output$simcount
EstMean[1]=mean(samp)
EstSD[1]=sd(samp)
SIMtotal[1]=simTotal
# Initial weights all equal
weight=rep(1,run)
for(t in 2:TT)
{
output=ABCimp(N,mstar,epsil[t],run,samp,weight)
samp=output$samp
weight=output$weight
simTotal=simTotal+output$simcount
SIMtotal[t]=simTotal
EstMean[t]=sum(samp*weight)/sum(weight)
EstSD[t]=sqrt(sum(samp^2*weight)/sum(weight)-EstMean[t]^2)
}
#
#
EstMean
EstSD
SIMtotal
tendC=proc.time()
tendC-tstartC
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