Maria1
November 24, 2021, 12:28pm
1
Hello,

I specified in the data block the mean for the prior distribution of the likelihood parameter. I expected this to work since it is only a real number. When running the model I get the message: “Warning no priors for parameter mu specified”. However, when specifying the sigma for the likelihood distribution in the data block this is not an issue.

```
data{
real x;
real y;
real mu_mean;
real sigma;
}
parameters{
real mu;
}
model {
// priors
mu ~ normal(mu_mean, 1);
// likelihood
y ~ normal(mu, sigma);
}
```

Is there a particular reason why this is not an option in Stan or how to solve this?

Best,
María

what interface are you using, and what version of Stan?

I did a cut-and-paste on the above program into file `foo.stan`

and then compiled and ran it on CmdStan 2.28.1 with the following data file:

```
{
"x" : 1.0,
"y" : 7.0,
"mu_mean" : 5.0,
"sigma" : 2.0
}
```

it all just worked.

```
~/.cmdstan/cmdstan-2.28.1> ./foo sample data file=foo.data.json
method = sample (Default)
sample
num_samples = 1000 (Default)
num_warmup = 1000 (Default)
save_warmup = 0 (Default)
thin = 1 (Default)
adapt
engaged = 1 (Default)
gamma = 0.050000000000000003 (Default)
delta = 0.80000000000000004 (Default)
kappa = 0.75 (Default)
t0 = 10 (Default)
init_buffer = 75 (Default)
term_buffer = 50 (Default)
window = 25 (Default)
algorithm = hmc (Default)
hmc
engine = nuts (Default)
nuts
max_depth = 10 (Default)
metric = diag_e (Default)
metric_file = (Default)
stepsize = 1 (Default)
stepsize_jitter = 0 (Default)
num_chains = 1 (Default)
id = 1 (Default)
data
file = foo.data.json
init = 2 (Default)
random
seed = 1686177967 (Default)
output
file = output.csv (Default)
diagnostic_file = (Default)
refresh = 100 (Default)
sig_figs = -1 (Default)
profile_file = profile.csv (Default)
num_threads = 1 (Default)
Gradient evaluation took 8e-06 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 2000 [ 0%] (Warmup)
Iteration: 100 / 2000 [ 5%] (Warmup)
Iteration: 200 / 2000 [ 10%] (Warmup)
Iteration: 300 / 2000 [ 15%] (Warmup)
Iteration: 400 / 2000 [ 20%] (Warmup)
Iteration: 500 / 2000 [ 25%] (Warmup)
Iteration: 600 / 2000 [ 30%] (Warmup)
Iteration: 700 / 2000 [ 35%] (Warmup)
Iteration: 800 / 2000 [ 40%] (Warmup)
Iteration: 900 / 2000 [ 45%] (Warmup)
Iteration: 1000 / 2000 [ 50%] (Warmup)
Iteration: 1001 / 2000 [ 50%] (Sampling)
Iteration: 1100 / 2000 [ 55%] (Sampling)
Iteration: 1200 / 2000 [ 60%] (Sampling)
Iteration: 1300 / 2000 [ 65%] (Sampling)
Iteration: 1400 / 2000 [ 70%] (Sampling)
Iteration: 1500 / 2000 [ 75%] (Sampling)
Iteration: 1600 / 2000 [ 80%] (Sampling)
Iteration: 1700 / 2000 [ 85%] (Sampling)
Iteration: 1800 / 2000 [ 90%] (Sampling)
Iteration: 1900 / 2000 [ 95%] (Sampling)
Iteration: 2000 / 2000 [100%] (Sampling)
Elapsed Time: 0.004 seconds (Warm-up)
0.011 seconds (Sampling)
0.015 seconds (Total)
~/.cmdstan/cmdstan-2.28.1> bin/stansummary output.csv
Inference for Stan model: foo_model
1 chains: each with iter=(1000); warmup=(0); thin=(1); 1000 iterations saved.
Warmup took 0.0040 seconds
Sampling took 0.011 seconds
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ -0.89 0.038 0.73 -2.4 -0.59 -0.40 382 34721 1.00
accept_stat__ 0.94 2.8e-03 8.9e-02 0.75 0.98 1.0 1.0e+03 9.2e+04 1.0e+00
stepsize__ 0.94 nan 6.0e-15 0.94 0.94 0.94 nan nan nan
treedepth__ 1.4 1.6e-02 4.9e-01 1.0 1.0 2.0 1.0e+03 9.1e+04 1.0e+00
n_leapfrog__ 2.9 5.8e-02 1.8e+00 1.0 3.0 7.0 9.4e+02 8.5e+04 1.0e+00
divergent__ 0.00 nan 0.0e+00 0.00 0.00 0.00 nan nan nan
energy__ 1.4 5.1e-02 1.0e+00 0.46 1.0 3.5 4.2e+02 3.8e+04 1.0e+00
mu 5.4 0.042 0.88 4.0 5.4 6.9 442 40159 1.0
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).
```

Maria1
November 29, 2021, 8:56am
3
Hi, thanks for replying. I am using pystan v3.3.0 and ran the model in pycharm and a jupyternotebook, both of them gave me the same error. Could this be the problem ?

what version of Stan is pystan using?

Maria1
November 29, 2021, 1:49pm
5
I’m assuming the latest version, Stan 2.26.1, since it was recently installed but how can this be checked?

Based on the documentation, it seems like

```
import httpstan
print(httpstan.stan.version())
```

should print the current Stan version for you

Maria1
November 30, 2021, 9:32am
7
Using 4.5.0 stan (had to run httpstan.version ) and pystan 3.2

Maria1
November 30, 2021, 12:09pm
8
I upgraded to 3.3.0 for pystan and 4.6.1 for stan and still get “Warning: The parameter mu_mean has no priors.” (the example provided above is a minimal working example, three parameters are actually used with three means tried to be specified in the data block and after called in the priors, the warning message appears for all of them).

that’s a pystan thing. there is no 4.5.0 Stan. it sounds like pystan is running the Stan compiler’s pedantic mode checks by default.

if this is annoying, try CmdStanPy: CmdStanPy 1.0.0rc3

Maria1
December 2, 2021, 8:49am
10
Ok, thanks for your reply and advice!