PyStan in CoCalc – Python 3 (Ubuntu Linux) Kernel

https://pystan.readthedocs.io/en/latest/index.html

ATTN: compiling uses ~2.5 gb of RAM

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import pystan

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pystan.__version__

'2.18.1.0'

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schools_code = """
data {
    int<lower=0> J; // number of schools
    vector[J] y; // estimated treatment effects
    vector<lower=0>[J] sigma; // s.e. of effect estimates
}
parameters {
    real mu;
    real<lower=0> tau;
    vector[J] eta;
}
transformed parameters {
    vector[J] theta;
    theta = mu + tau * eta;
}
model {
    eta ~ normal(0, 1);
    y ~ normal(theta, sigma);
}
"""

schools_dat = {
    'J': 8,
    'y': [28, 8, -3, 7, -1, 1, 18, 12],
    'sigma': [15, 10, 16, 11, 9, 11, 10, 18]
}

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sm = pystan.StanModel(model_code=schools_code)
sm

INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_19a09b474d1901f191444eaf8a6b8ce2 NOW.

<bound method StanModel.show of <pystan.model.StanModel object at 0x7f315b4a5ba8>>

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fit = sm.sampling(data=schools_dat, iter=1000, chains=1, n_jobs=1)
fit

WARNING:pystan:1 of 500 iterations ended with a divergence (0.2 %).
WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.

Inference for Stan model: anon_model_19a09b474d1901f191444eaf8a6b8ce2.
1 chains, each with iter=1000; warmup=500; thin=1; 
post-warmup draws per chain=500, total post-warmup draws=500.

           mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat
mu         8.43    0.73   5.73  -1.14   4.65   8.07  11.33  24.13     61    1.0
tau        6.55    0.41   5.22   0.32   2.67   5.22   9.23  18.99    164   1.02
eta[1]     0.38    0.06   0.92   -1.5  -0.22    0.4   1.06   2.07    279    1.0
eta[2]   6.8e-3    0.04   0.94   -1.7  -0.62  -0.05    0.6   2.04    440    1.0
eta[3]     -0.2    0.04   0.94  -1.92  -0.92  -0.21   0.51   1.66    504    1.0
eta[4]    -0.05    0.05   0.89  -1.63  -0.76  -0.03   0.57    1.8    286   1.01
eta[5]    -0.45    0.06   0.96  -2.29  -1.05   -0.4   0.13   1.44    253    1.0
eta[6]    -0.32    0.05   0.92   -2.1  -0.86  -0.32   0.22   1.51    296    1.0
eta[7]      0.3    0.04   0.86  -1.47  -0.24   0.29   0.88    2.1    442    1.0
eta[8]  -5.5e-3    0.05   0.99  -1.95  -0.65  -0.02   0.63   1.95    397    1.0
theta[1]  11.81    0.69   8.47  -0.71   6.24  10.16  15.66  32.93    152    1.0
theta[2]   8.06    0.27   6.32  -4.13    4.2   8.24  11.78   21.4    547    1.0
theta[3]   6.56    0.35   7.72 -10.19   1.91   6.84  11.29   21.5    478    1.0
theta[4]   7.67    0.26   6.09  -3.56   3.84   7.62  11.19  20.66    533    1.0
theta[5]   4.47    0.28   6.07  -9.01   0.82   4.92   8.41  16.15    461   1.01
theta[6]    5.9    0.34   6.35  -8.13   2.03   5.63  10.02  18.89    345    1.0
theta[7]  10.92    0.53    7.4  -0.04    5.8   9.89  14.88  30.73    197    1.0
theta[8]   8.74    0.49   8.13  -5.33   3.94   8.21  12.92  27.23    276    1.0
lp__      -4.93     0.2   2.52 -10.29  -6.39  -4.77  -3.15  -0.51    158    1.0

Samples were drawn using NUTS at Sat Jan 12 12:10:45 2019.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at 
convergence, Rhat=1).

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