Talk:Bayesian network
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[edit] history
Wasnt there some big history to bayesian networks? They were one of the first statisical processing methods invented, no?
The article as it stands (2003/12/26) limits the definition unnecessarily. I'm going to edit the article to address these points: (1) a node can represent any kind of variable, not just discrete random variables; variables need not be discrete, and they need not be random. (2) the arcs don't represent correlation; correlation in probability theory has a certain well-defined meaning which is not applicable here. What arcs do represent is conditional dependence. (3) "Conditional probability table" assumes that the variables involved are discrete; need to allow for continuous variables. (4) The list of applications can be expanded.
I've addressed (or tried to) items (1) through (3) above. Wile E. Heresiarch 07:41, 27 Dec 2003 (UTC)
An example would be very helpful in this article. Banno 01:05, Jul 7, 2004 (UTC)
[edit] learning
It might be interesting to put some comments about the learning of the BNs
--Response: I did this implicitly by saying that distributions could be parameterized and discussing the use of EM (expectation-maximization) in estimating these parameters from data. Also, there is already a paragraph on learning the structure of BN's. I think this covers the fundamental "learning" areas, though the section on parameters and EM could be expanded a bit.
[edit] Dynamic Bayesian network
I was disappointed to see the DBN link redirect back to BN. In this case someone needs to write a section discussing the specialization to DBN's, and the special cases of hidden Markov models, Kalman filters, and switching state-space models, and their applications in tracking and segmentation problems
[edit] Way, way, wayyyyy too technical
No doubt it's a wonderfully accurate and concise explanation exactly what a Bayesian network is but it's useless to those of us who aren't striving for that PhD in math. At the very least it needs an introduction stating, in plain english, what such a network is. I can't be the one to write that introduction because after reading the article I have less of a clue about what they are then before I read it. The sort of detail in this article is great as a stand-alone webpage or reference, but it's not an encyclopedia entry. Frustrating.
[edit] Graphical?
Seems like a graphical tool should use some graphics... It would be a lot easier to understand with one of the example networks from the reference material. For example, Murphy's Fig. 1.
