I checked the parameters of the distributions in this program in Stan vs PyMC3. I think they have the same meaning. Please let me know if you know otherwise.
Below are the code snippets and results for Stan
data{
matrix[10,10] x;
real weight[10];
vector[10] y;
}
parameters{
vector[10] w;
real b;
}
model{
w~normal(1.0,10.0);
b~weibull(93.19563293457031,46.43210220336914);
y~logistic(x*w+b,1.0);
}
Result with NUTS:
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ 325 6.6e-02 2.6e+00 320 325 328 1.5e+03 2.2e+03 1.0e+00
accept_stat__ 0.94 1.3e-03 8.3e-02 0.77 0.98 1.00 4.1e+03 5.9e+03 1.0e+00
stepsize__ 0.074 3.5e-03 4.9e-03 0.071 0.071 0.083 2.0e+00 2.8e+00 9.3e+13
treedepth__ 5.4 3.8e-02 6.0e-01 4.0 5.0 6.0 2.5e+02 3.6e+02 1.0e+00
n_leapfrog__ 56 7.7e-01 2.2e+01 31 63 95 8.5e+02 1.2e+03 1.0e+00
divergent__ 0.00 -nan 0.0e+00 0.00 0.00 0.00 -nan -nan -nan
energy__ -319 9.3e-02 3.5e+00 -324 -320 -313 1.4e+03 2.0e+03 1.0e+00
w[1] -13 1.2e-01 4.8e+00 -21 -13 -5.0 1.5e+03 2.2e+03 1.0e+00
w[2] 2.6 1.2e-01 5.1e+00 -5.8 2.7 11 1.9e+03 2.7e+03 1.0e+00
w[3] -17 8.1e-02 3.1e+00 -22 -17 -12 1.5e+03 2.1e+03 1.0e+00
w[4] 4.4 1.3e-01 4.8e+00 -3.4 4.5 12 1.5e+03 2.1e+03 1.0e+00
w[5] -6.7 1.0e-01 5.1e+00 -15 -6.7 1.6 2.4e+03 3.4e+03 1.0e+00
w[6] -4.2 7.6e-02 3.9e+00 -11 -4.2 2.2 2.7e+03 3.8e+03 1.0e+00
w[7] -4.1 1.0e-01 4.2e+00 -11 -4.2 2.9 1.6e+03 2.2e+03 1.0e+00
w[8] -6.5 5.6e-02 3.0e+00 -11 -6.5 -1.5 2.8e+03 4.0e+03 1.0e+00
w[9] -8.6 6.9e-02 3.1e+00 -14 -8.6 -3.3 2.0e+03 2.9e+03 1.0e+00
w[10] 0.71 5.9e-02 3.1e+00 -4.4 0.74 5.7 2.7e+03 3.9e+03 1.0e+00
b 46 1.8e-02 7.5e-01 45 46 47 1.7e+03 2.4e+03 1.0e+00