If I understand it right, both use Bayes Theorem to generate an acyclic graph and calculate percentages based on functions applied at every node.
What is the difference?
+1
A:
One simple and fundamental difference is Acyclic Graph != Tree
For example, a->b<-c is not a tree (it has two roots), but it is an acyclic graph.
I am not well versed in decision trees, but I am well versed in Bayesian Networks. Here are some things that you can do with Bayesian Networks that I am not sure if you can do with a decision tree. Researching how to do these things with a decision tree may reveal interesting differences.
- Compute the joint probability table between the variables
- Determine if two variables are conditionally independent
- Given some evidence, determine the distribution of the non-evidence variables given the evidence
Carlos Rendon
2010-08-10 22:02:29
Can the Acyclic Graph generated by a Bayesian Network be a Forest then? Because otherwise they both generate Trees since a connected Acyclic Graph is a Tree.
iceburn
2010-08-11 23:01:01
Yes. It is possible for two variables to be independent. In which case they are not connected.Also a Bayesian Network does not "generate" a graph, it is a graphical representation of conditional independence relationships between the variables of a probability distribution.
Carlos Rendon
2010-08-11 23:33:34