I am trying to fix priors for my dataset, however any change I make, does not reflect in the output samples. To make sure priors are working as expected, I have reduced the number of samples to minimum to reduce effect from data.

Say I use the model as:

```
parameters {
real<upper=0.9999> alpha[C];
real<lower=0> k[C];
}
model {
alpha ~ normal(-0.5,0.4);
k ~ normal(7.5,0.5);
}
# samples_fitted = self.model.sample(num_chains=4, num_samples=2000, num_warmup=500)
samples_fitted = self.model.sample(num_chains=4, num_samples=1, num_warmup=1)
df_fit = samples_fitted.to_frame()
```

and take 4 samples, parameters look like this: (I draw 4 samples and take the mean)

- k.1 2.66
- k.2 2.24
- k.3 1.75
- k.4 0.82
- alpha.1 0.59
- alpha.2 0.60
- alpha.3 0.57
- alpha.4 0.60

Say now I update the model as:

```
model {
alpha ~ normal(0.5,0.4);
k ~ normal(0.2,0.1);
}
```

the samples are now:

- k.1 1.41
- k.2 2.35
- k.3 0.69
- k.4 1.64
- alpha.1 0.51
- alpha.2 0.65
- alpha.3 0.49
- alpha.4 0.34

which are about similar to before and make me wonder if the prior is having any effect.

Any clarity on this would be really helpful!

Thank you!