What is the difference between a Decision Tree and a Bayesian Network?

If 开发者_如何学JAVAI 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?

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