[R] Bayesian Networks with deal

[Spencer Graves]

	  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]]
> 
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