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

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

 

you are kindly asked to include the complete citation if you used this material in a publication

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Code 6.16 Negative binomial model in Python using pymc3

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import numpy as np
import pandas
import pylab as plt
import pymc3 as pm
from scipy.stats import uniform, binom, nbinom
import statsmodels.api as sm

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# Data
np.random.seed(141)                                                    # set seed to replicate example
nobs= 2500                                                                   # number of obs in model 

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x1 = binom.rvs(1, 0.6, size=nobs)                                # categorical explanatory variable
x2 = uniform.rvs(size=nobs)                                        # real explanatory variable

theta = 0.303

xb = 1 + 2 * x1 - 1.5 * x2                                             # linear predictor

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exb = np.exp(xb)
nby = nbinom.rvs(exb, theta)

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df = pandas.DataFrame({'x1': x1, 'x2':x2,'nby': nby})   # re-write data

# Fit
niter = 10000                                                                  # parameters for MCMC

with pm.Model() as model_glm:
    # define priors
    beta0 = pm.Flat('beta0')
    beta1 = pm.Flat('beta1')
    beta2 = pm.Flat('beta2')

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    # define likelihood
    linp = beta0 + beta1 * x1 + beta2 * x2
    mu = np.exp(linp)
    mu2 = mu * (1 - theta)/theta                                     # compensate for difference between
                                                                                      # parametrizations from pymc3 and scipy    

  
    y_obs = pm.NegativeBinomial('y_obs', mu2, theta, observed=nby)

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    # inference
    start = pm.find_MAP()                                             # Find starting value by optimization
    step = pm.NUTS()
    trace = pm.sample(niter, step, start, progressbar=True)

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# print summary to screen
pm.summary(trace)

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# show graphical output
pm.traceplot(trace)
plt.show()

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