Hi, any suggestion about how to improve the speed is appreciated.

I have the following simple model and I was sampling it using STAN, JAGS and BUGS. The model is essentially linear regression with one breakpoint, i.e., regression using two straight lines that are connected.

Fitting the same model (5000 samples * 3 chains) on eight data sets totally takes 25 seconds in JAGS, 50 seconds in BUGS and 65 seconds in STAN. It surprises me because STAN is a newer language and I thought it was going to be faster. Speed is very important to me because I have >20,000 such datasets to fit in a short period, so it has to be fast.

Alternatively, is that 5000 samples in STAN is >> 5000 samples in BUGS? if then, which statistic should I observe to make sure the models have the same effective number of samples?

```
/*
*normal regression example with change point
*/
data {
real<lower=0> ymax;
real<lower=0> xmax;
int<lower=1> N; //the number of students
vector<lower=0,upper=50>[N] y; //the exam mark
vector<lower=0,upper=50>[N] X; //the school assessment mark
}
parameters {
real<lower=0.05,upper=0.2> s; //Scale parameter for laplace distribution
real<lower=0> a1; //slope before break point
real<lower=0> a2; //slope after break point
real<lower = 0, upper = xmax> bp; // the breakpoint age, with some constraints
}
transformed parameters {
vector<lower=0.00000000000000000001,upper=50>[N] linpred;
real<lower=-ymax,upper=ymax> b1; //intercept
real<lower=-ymax,upper=ymax> b2; //intercept
b2 = ymax - (a2)*xmax;
b1 = b2 + (a2-a1)*bp;
for (i in 1:N) {
if (X[i] < bp) {
linpred[i] = fmax(a1*X[i]+b1,0.00000000000000000001);
} else {
linpred[i] = fmax(a2*X[i]+b2,0.00000000000000000001);
}
}
}
model {
s ~ normal(0.125,2.24) T[0.05,0.2];
a1 ~ lognormal(-0.01,0.82);
a2 ~ lognormal(0.02,0.82);
y ~ double_exponential(log(linpred),s);
}
```