From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press

(c) 2017,  Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida 

 

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Code 10.13 Bernoulli model in Python using Stan, for assessing the relationship between bulge size and the fraction of red spirals

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import numpy as np
import pandas as pd
import pystan 
import statsmodels.api as sm

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# Data
path_to_data = 'https://raw.githubusercontent.com/astrobayes/BMAD/master/data/Section_10p6/Red_spirals.csv'

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# read data
data_frame = dict(pd.read_csv(path_to_data))
x = np.array(data_frame['fracdeV'])

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# prepare data for Stan
data = {}
data['X'] = sm.add_constant((x.transpose()))
data['Y'] = np.array(data_frame['type'])
data['nobs'] = data['X'].shape[0]
data['K'] = data['X'].shape[1]

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# Fit
stan_code="""
data{
    int<lower=0> nobs;                                 # number of data points
    int<lower=0> K;                                      # number of coefficients
    matrix[nobs, K] X;                                   # bulge size
    int Y[nobs];                                              # galaxy type: 1 - red, 0 - blue
}
parameters{
    vector[K] beta;                                         # linear predictor coefficients
}
model{
    # priors and likelihood
    for (i in 1:K) beta[i] ~ normal(0, 100);

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    Y ~ bernoulli_logit(X * beta);
}
"""

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# Run mcmc
fit = pystan.stan(model_code=stan_code, data=data, iter=6000, chains=3,
                            warmup=3000, thin=1, n_jobs=3)

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# Output
print(fit)

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