[R] Bayesian Networks with deal

Spencer Graves spencer.graves at pdf.com
Thu Jun 22 18:13:01 CEST 2006

	  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! 

	  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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