我做leverage effect model.退火了一万次,再迭代四万次,可结果好像不对啊,前面四个模型倒没问题.
model{
### likelihood: joint distribution of ys
for (t in 1:(N-1)) {
Ymean[t] <- rho/tau*exp(0.5*theta[t])*(theta[t+1]-mu- phi*(theta[t]-mu));
Yisigma2[t] <- 1/(exp(theta[t])*(1-rho*rho));
Y[t] ~dnorm(Ymean[t],Yisigma2[t]);
}
Ymean[N]<- mu-phi*(theta[N]-mu);
Yisigma2[N] <- 1/(exp(theta[N]));
Y[N] ~dnorm(Ymean[N],Yisigma2[N]);
#####################################
theta0 ~dnorm(mu,itau2);
thetamean[1] <- mu + phi*(theta0-mu);
theta[1] ~dnorm(thetamean[1],itau2)I(-5,5);
for (t in 2:N) {
thetamean[t] <- mu + phi*(theta[t-1]-mu);
theta[t] ~dnorm(thetamean[t],itau2)I(-4,4);
}
###### prior distributions
phistar ~ dbeta(20,1.5);
phi <- 2*phistar-1; #(expected value 0.86)
mu ~ dnorm(0,0.1);
beta <- exp(mu/2); #beta:= exp(mu/2) is a constant scaling factor
itau2 ~ dgamma(2.5,0.025); #(expected value 100)
tau <- sqrt(1/itau2);
rho ~ dunif(-1,1);
}
是哪个地方出了问题呢.
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