Since I haven't seen a reply to this, I will offer a couple of comments. I've never used "deal", but it sounded interesting, so I thought I'd look at it.
Have you looked at Susanne G. Bøttcher and Claus Dethlefsen. deal: A Package for Learning Bayesian Networks. Journal of Statistical Software, 8(20), 2003, and the deal reference manual downloadable under "documentation" from "www.math.aau.dk/~dethlef/novo/deal"? If yes and you still would like more help from this listserve, please submit another post including a simple, self-contained example explaining something you've tried and why it doesn't seem to answer your question? (This is suggested in the posting guide! 'www.R-project.org/posting-guide.html'.) This documentation might answer your questions. Even though I've not read them, I will guess potential answers to your two questions, hoping some other reader may disabuse us both of our ignorance: From what I saw in the examples, I would guess that "deal" supports two types of distributions: normal and finite (discrete). If so, it does NOT support a Poisson. If it were my problem and I still held that view after reviewing this documentation, I might write to the maintainer [listed with help(package="deal")] and ask him for suggestions. Then if it were sufficiently important, I might think about how I would modify the code to allow for a Poisson. Regarding simulations, have you looked at "rnetwork", which provides "simulation of data sets with a given dependency structure"? Hope this helps, Spencer Graves Carsten Steinhoff wrote: > 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html