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Created Mar 17, 2011
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Zero-inflated Poisson example using simulated data.
#!/usr/bin/env python
Zero-inflated Poisson example using simulated data.
Created by Chris Fonnesbeck on 2008-06-06.
Distributed under the MIT license:
import pymc as pm
import numpy as np
# True parameter values
mu_true = 5
psi_true = 0.75
n = 100
# Simulate some data
data = np.array([pm.rpoisson(mu_true)*(np.random.random()<psi_true) for i in range(n)])
# Uniorm prior on Poisson mean
mu = pm.Uniform('mu', 0, 20)
# Beta prior on psi
psi = pm.Beta('psi', alpha=1, beta=1)
@pm.observed(dtype=int, plot=False)
def zip(value=data, mu=mu, psi=psi):
""" ZIP likelihood """
# Initialise likeihood
like = 0.0
# Loop over data
for x in value:
if not x:
# Zero values
like += np.log((1.-psi) + psi*np.exp(-mu))
# Non-zero values
like += np.log(psi) + pm.poisson_like(x, mu)
return like
if __name__=="__main__":
M = pm.MCMC(locals())
M.sample(100000, 50000, verbose=2)

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Volodymyrk commented Jul 23, 2016

Chris, do you know if there is a pymc3 version of this model?


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