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I'm trying a very simple model: fitting a Normal where I assume I know the precision, and I just want to find the mean. The code below seems to fit the Normal correctly. But after fitting, I want to sample from the model, ie generate new data which is similar to my data variable. I know I can use trace("mean") to get samples for the mean variable. But how can I get new samples from the model itself?

I've looked at docs eg http://pymc-devs.github.io/pymc/database.html#accessing-sampled-data. I've also looked at quite a few examples, eg the mining disasters, and several from the Probabilistic Programming notebooks, and none mention this. I (more or less an MCMC beginner) expected that sampling from the fitted model was the whole point! What am I missing?

from pymc import *
data = np.array([-1, 0, 4, 0, 2, -2, 1, 0, 0, 2, 1, -3, -1, 0, 0, 1, 0, 1])
mean = Uniform("mean", -4, 4)
precision = 2.0**-2
obs = Normal("obs", mean, precision, value=data, observed=True)
model = Model( {"mean": mean, "obs": obs})
mcmc = MCMC(model)
mcmc.sample(10000, 1000, 1)
# I can get samples for the "mean" variable
mean_samples = mcmc.trace("mean")[:]
hist(mean_samples)
# but how can I do the equivalent of mcmc.trace("obs")?
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  • $\begingroup$ Exactly the question I had! Wondering if the predictive sampling is simplified in pymc3... $\endgroup$ – Vladislavs Dovgalecs Apr 15 '16 at 23:14
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You are looking the what's called the predictive distribution. To include this is very simple. Before creating the Model, add the additional stochastic variable:

predictive = mc.Normal( "predictive", mean, precision )
model = Model( {"mean": mean, "obs": obs, "pred":predictive})

...

predictive_traces = mcmc.trace("predictive")[:]
hist( predictive_traces )

Artificial data from the fitted model

This will generate artifical data from the fitted model. Thanks for bringing this oversight to my attention, I'll include it in the BMH project.

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Landed here several years later when looking for the same thing using PyMC3, so I am going to leave an answer relevant to the new version: (from Posterior Predictive Checks).

ppc = pm.sample_ppc(trace, samples=500, model=model, size=100)

Now, ppc contains 500 generated data sets (containing 100 samples each), each using a different parameter setting from the posterior.

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