sturlamolden wrote:
Alex wrote:
I wonder if it is possible in python to produce random numbers
according to a user defined distribution?
Unfortunately the random module does not contain the distribution I
need :-(
There exist some very general algorithms to generate random numbers
from arbitrary distributions.
The most notable of these are "Markov Chain Monte Carlo", e.g. the
Metropolis-Hastings algorithm. It is very easy to implement in any
programming language. The nature MCMC algorithms makes it inefficient
when implemented in pure Python. But you can get tremendous speedup by
simulating multiple Markov chains in parallel, by means of vectorizing
with NumPy.
A relative of Metropolis-Hastings which may also be applicable to your
problem is pure "rejection sampling". It is far less efficient, but
produces no autocorrelation in the samples.
I don't know. I think the certainty that rejection sampling actually gives you
the desired distribution as opposed to MH's uncertainty is very much a
worthwhile tradeoff for univariate distributions. Sure, you throw away fewer
samples, but you know that MH isn't throwing away samples that it ought to.
Personally, I view MH as a last resort when the dimensionality gets too large to
do anything else. But then, that's just my opinion.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
--
http://mail.python.org/mailman/listinfo/python-list
