/usr/lib/python2.7/dist-packages/pymc/examples/disaster_model.py is in python-pymc 2.2+ds-1.1.
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A model for the disasters data with a changepoint
changepoint ~ U(0, 110)
early_mean ~ Exp(1.)
late_mean ~ Exp(1.)
disasters[t] ~ Po(early_mean if t <= switchpoint, late_mean otherwise)
"""
from pymc import *
from numpy import array, empty
from numpy.random import randint
__all__ = ['disasters_array', 'switchpoint', 'early_mean', 'late_mean', 'rate', 'disasters']
disasters_array = array([ 4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])
# Define data and stochastics
switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')
early_mean = Exponential('early_mean', beta=1.)
late_mean = Exponential('late_mean', beta=1.)
@deterministic(plot=False)
def rate(s=switchpoint, e=early_mean, l=late_mean):
''' Concatenate Poisson means '''
out = empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out
disasters = Poisson('disasters', mu=rate, value=disasters_array, observed=True)
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