I’m newbie using STAN. I’m trying to perform a Bayesian mixture model (mixing Gaussian and GEV distributions). However, the model has convergence problem. Any comments on improving it.Thanks!

```
functions{
real gev_lpdf(vector y, real loc_gev, real scale_gev, real shape_gev) {
vector[rows(y)] z;
vector[rows(y)] lp;
real loglike;
int N;
N = rows(y);
if (fabs(shape_gev) > 1e-10) {
for(i in 1:N){
z[i] = 1 + shape_gev * (y[i] - loc_gev) / scale_gev;
lp[i] = (1 + 1 / shape_gev) * log(z[i]) + pow(z[i], -1 / shape_gev);
}
loglike = - sum(lp) - N * log(scale_gev);
} else {
for (i in 1:N){
z[i] = (y[i] - loc_gev) / scale_gev;
lp[i] = z[i] + exp(-z[i]);
}
loglike = - sum(lp) - N * log(scale_gev);
}
return(loglike);
}
}
data {
int<lower = 0> N;
vector[N] y;
}
transformed data {
real miny;
real maxy;
miny = min(y);
maxy = max(y);
}
parameters {
real<lower=-0.5,upper=0.5> shape_gev;
real<lower=0> scale_gev;
real<lower= (shape_gev > 0 ? miny : negative_infinity()),
upper= (shape_gev > 0 ? positive_infinity() : maxy) > loc_gev;
real<lower=0, upper=0.5> loc_nor;
real<lower=0 > scale_nor;
real<lower=0, upper=1> theta;
}
model {
shape_gev ~ normal(-0.5, 0.5);
loc_gev ~ normal(0.06, 10);
scale_gev ~ normal(0.09, 10);
loc_nor ~ normal(0.23, 25);
scale_nor ~ normal(0.03, 10);
theta ~ uniform(0,1);
target += log_mix(theta,
normal_lpdf(y | loc_nor, scale_nor),
gev_lpdf(y | loc_gev,scale_gev,shape_gev));
}
```