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PyMC is a Python library for performing Bayesian inference using MCMC. It is a Python equivalent to JAGS and BUGS.

1
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So the problem with this was actually with the BinaryMetropolis sampler, a problem I only discovered by stumbling on this post. I adjusted the scaling parameter of the sampler and after about 35k sam …
answered Dec 10 '15 by degenerate hessian
6
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2answers
'); axes.set_xlabel('X1'); EDIT: I've attempted the same model in pymc as follows: import pymc as mc p = mc.Uniform('p', 0, 1, value=.5) #Proportion in each mixture alpha = mc.Normal('alpha', mu=0, tau=1 …
asked Dec 8 '15 by degenerate hessian
0
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I don't see the need for making the switchpoint a random variable in this case, as you know when the treatment is happening. You could instead treat this as a kind of paired T-test, with each observe …
answered Jan 28 '16 by degenerate hessian
1
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I think the short answer is no. Pymc is not really designed for Bayesian networks but for Bayesian data analysis, which are not the same thing. There is some overlap in that both use a DAG …
answered Aug 29 '16 by degenerate hessian
4
votes
3answers
I'm trying to understand factor potentials from the PyMC documentation, but need some help on the implementation piece--or it may turn out that I am misunderstanding how potentials work altogether … posterior distribution. Why does the posterior distribution of potentialCheck contain these values? Obviously, I am doing something wrong... from pymc import DiscreteUniform, Exponential …
asked Apr 14 '14 by degenerate hessian