No need to propagate - just find the row of the cp-table of the next node
corresponding to the values of the parents already selected.
- -----Original Message-----
From: Russell Almond <[EMAIL PROTECTED]>
To: [EMAIL PROTECTED] <[EMAIL PROTECTED]>
Date: Friday, December 03, 1999 12:05 AM
Subject: Re: [UAI] Learning a database of cases from a given structure.
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>
> I'm working on an algorithm for learning the structure of a bayesian
> network from data using a bayesian approach. In order to evaluate my
> algorithm, I'm looking for a free and simple program that create a
database
> of cases from a given structure. Once the database detected, I will run
my
> algorithm on it and compare the initial structure with the structure
detected.
>
>
>The algorithim is so simple that any Bayes net propagation engine could in
>principle do it. (If it comes as a shared library, it is relatively
>simple to write a wrapper program to do this. I did it recently using
>the Ergo DLL.) First, select a node and get its marginal
>distribution, randomly select a value according to that distribution.
>Next, set that node to the randomly selected value and propagate. Now
>choose another node, and find its marginal distribution (conditioned
>on the value of the first node). Randomly choose its value according
>to that conditional distribution. When you have instantiated all of
>the nodes, you will have sampled a value from this Bayes net.
>
> Can anyone save me the time and recommend me a link, an adress or a
person
> working on such programs?
>
>Free means you need to do work yourself to get it going. There are a
>couple of options.
>
>a) BELIEF had this capbility. It is available in the CMU AI
>reposititory, but it is written in a rather old (pre-ANSI) version of
>Common Lisp so it needs a few fix-ups to work in a modern Lisp.
>
> <a
href="http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/reasonng/p
robabl/belief/0.html">CMU Artificial Intelligence Library</a>
>
>b) BUGS will also sample from an arbitrary graphical model (includinge
>Bayes nets). However, it will take you a bit of effort to learn to
>use it and describe your model in BUGS. Bugs is at:
><a href="http://www.mrc-bsu.cam.ac.uk/bugs">England</a>
><a href="ftp://muskie.biostat.umn.edu/mirror/methodology/bugs">US
Mirror</a>
>
>c) Joe Schafer the Penn State Statistics Department has some programs
>for multiple imputation in missing data that might be pressed into
>service. His programs cover multivariate-multinomial (Bayes nets),
>multivariate normal and conditional Gaussian models.
>
>Good luck.
>
> --Russell Almond
>Educational Testing Service
>Research Statistics Group, 15-T
>Princeton, NJ 08541
>Phone: 609-734-1557 FAX: 609-734-5420
>Email: [EMAIL PROTECTED], [EMAIL PROTECTED]
>http://www.stat.washington.edu/bayes/almond/almond.html
>[Remove -- from email addresses]
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