I’m trying to debug my model for which Stan sometimes says the initial parameters have a loglikelihood of -inf. However, this is not true since I can verify it with my own loglikelihood function (I’m almost certain both are correct). When using `print(target())`

in the first line of my model block, I see that it is -inf. I’m confused because I assumed the target was always 0 at the beginning of the block. For that reason, I have no idea what is the problem. Is the jacobian for the unrestricted parameters computed before the block? If so, what does it mean to have -inf?

Important things to note:

- I’m letting Stan find out the step size and the metric once, then I fix the values (I call Stan multiple times);
- the issue seems to occur only when there is no adaptation (could it be why?).

Here is an example for which this happens:

```
n:30
beta_:2.5
theta:[0.513179,-2.96086,1.55316,0.15685,1.22294,0.0186857,-0.302652,1.39564,-0.460454,0.953593,-0.755759,-2.83643,-1.77762,0.979781,1.3331,1.88611,-1.54583,-1.75944,1.80086,-1.96354,1.21745,1.00603,0.952342,-1.22556,-2.93655,-0.201251,1.43891,-1.09986,2.05508,-0.68469]
edge:[0,1,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,1,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]
kappa_min:1.03527
gamma_:2.5
radius_div_mu:77.3876
```

and here is the model:

```
functions {
real angular_distance(real theta1, real theta2) {
return pi()-abs(pi()-abs(theta1-theta2));
}
}
data {
int<lower=3> N;
real<lower=0> beta_;
array[N] real<lower=-pi(), upper=pi()> theta;
array[(N*(N-1))%/%2] int<lower=0, upper=1> edge;
real<lower=0> kappa_min;
real<lower=0> gamma_;
real<lower=0> radius_div_mu;
}
transformed data{
array[(N*(N-1))%/%2] real<lower=0, upper=pi()> distances;
int r=1;
for (i in 1:N-1) {
for (j in i+1:N) {
distances[r] = angular_distance(theta[i], theta[j]);
r+=1;
}
}
}
parameters {
array[N] real<lower=kappa_min> kappa;
}
model {
print("at beginning of model: ", target());
for (i in 1:N) {
target += -gamma_*log(kappa[i]);
}
int k=1;
for (i in 1:N-1) {
for (j in i+1:N) {
if (edge[k]==1) {
if (distances[k] > 0) {
target += -log1p_exp(beta_*(log(distances[k])+log(radius_div_mu/kappa[i]/kappa[j])));
}
}
else {
target += -log1p_exp(-beta_*(log(distances[k])+log(radius_div_mu/kappa[i]/kappa[j])));
}
k+=1;
}
}
}
```

Thanks for the help!