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The following model is part of the tutorial of PyMC, named disaster_model.py and can be imported in the main code to be used as a model:

"""
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)

Now one can do a sampling from distributions using MCMC Metropolis Hasting algorithm to get posterior distribution of parameters. 

from pymc.examples import disaster_model
from pymc import MCMC
M = MCMC(disaster_model)
M.sample(iter=10000, burn=1000, thin=10)

Now my problem is that suppose after this sampling I achieve new data. How can I update my posterior distributions afterwards? Basically how can implement online learning using PyMC?

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1 回答 1

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您需要为更新指定一个新模型。这样做的原因是，现在您将拥有可用于未知参数的信息先验。具体来说，您DiscreteUniform的开关点将是分类或多项式（n=1），并且速率参数可能都是正态分布的。您可以将这些先验（使用几种方法之一）拟合到模型第一次运行的后验样本。如果您计划重复更新，您可以轻松地以编程方式进行此更新。

于 2013-10-23T21:08:10.273 回答