

A tutorial on learning with Bayesian networks (1996) [pdf] - yati
http://research.microsoft.com/pubs/69588/tr-95-06.pdf

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mden
Can someone tell me if the following makes sense? I want to use Bayesian
networks for sysops. What I have in mind is hooking logging, process
information (e.g. resource usage), and other information as nodes to a
Bayesian network and then training it in a production environment. From my
admittedly small understanding, it seems like a properly configured network
like that will be able to triage issues and even infer causes and take
potential actions to correct them. Of course you would need to limit it's
ability to act so it doesn't decide to hose your entire system. Since it has
the power in some sense to observe, it can even train itself by observing the
results of its actions.

Anyone know of anything like this being used or have ideas why this would be
stupid to do outside of it being difficult to get right?

~~~
mamp
A Bayesian network will update probabilities based on evidence - You can use
data to learn conditional probabilities and an algorithm will update the joint
probability distribution. However to suggest actions or further diagnostic
tests you will need decision and utility nodes. This is called an influence
diagram or decision model. With these extensions you can determine the best
action and value of information (test vs act).

The actions are based on utilities (cost/benefit) so if you have a potential
very bad outcome of an action and an alternative (eg messaging to someone)
then the system will act in a rational way consistent with the utility model.

I suggest you look at a package that can handle both Bayesian networks and
influence diagrams, such as Hugin, Norsys or Genie. There are others but I
haven't tried them.

------
blueblob
See also Bayesian Networks Without Tears[1]

[[http://www.cs.ubc.ca/~murphyk/Bayes/Charniak_91.pdf](http://www.cs.ubc.ca/~murphyk/Bayes/Charniak_91.pdf)]

