Random seed

[Thomas Wiecki]

I am running multiple chains of my model in parallel by using
multiprocessing.pool(). However, I think that the seeds get
initialised to the same values in each process as the traces are
_very_ similar (all starting values are the same). Is this correct? If
yes, how can I set the initial seed pymc uses. If not, what else could
be the problem here?

[Chris Fonnesbeck]

Hi Thomas,

At present, the way to supply a random number seed is via the rseed
for each Stochastic in your model:

x = Normal('x', mu=mu, tau=tau, rseed=342354321)

Alternately, you could supply different initial values yourself, via
the value argument.

cf

On Jun 10, 5:30 pm, Thomas Wiecki <thomas.wie...@googlemail.com>
wrote:

[Thomas Wiecki]

Hi Chris,

thanks for your help. Unfortunately, setting rseed fot the stochastics
did not change the first value being drawn. This is rather curios to
me. Any ideas?

-Thomas

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[Thomas Wiecki]

OK, rseed did not change anything. But numpy.random.seed(rnd) did the trick.

Thanks!

-Thomas