Hello,
 
I want to use R to model a bayesian belief network of frequencies for system
failiures in various departments of a company.
 
For the nodes I want to use a poisson-distribution parameterized with expert
knowledge (e.g. with a gamma prior).
Then I want to fill in learning-data to improve the initial estimates and
get some information about possible connections.
Later I want to simulate dependend random variables from the network
 
I tryed to use the package "deal" for that task, which is as far as I know
the best (and only?) R-package for that task.
But a few questions rose that I could not solve with the documentation:
 
(1) Is it possible to parameterize the prior distribution (for example
(dpois(x,lambda=60) directly and non gaussian ?
 
(2) If I've chosen a structure, can I simulate dependend states that are non
gausian distributed?
 
Thank you for any idea!
 
Regards, Carsten

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