Come up with your own problem involving reasoning with evidence and uncertainty.

Write down a text description of the problem, then model it using a Bayesian network.

Make the problem sufficiently complex that your network has at least 8 nodes and is multiply-connected (i.e., not a tree or a polytree).

1. Show the beliefs for each node in the network before any evidence is added.

2. Which nodes are d-separated with no evidence added?

3. Which nodes in your network would be considered evidence (or observation)

nodes? Which might be considered the query nodes? (Obviously this depends on the domain and how you might use the network.)

4. Show how the beliefs change in a form of diagnostic reasoning when evidence about at least one of the domain variables is added. Which nodes are d-separated with this evidence added?

5. Show how the beliefs change in a form of predictive reasoning when evidence about at least one of the domain variables is added. Which nodes are d-separated with this evidence added?

6. Show how the beliefs change through “explaining away” when particular combinations of evidence are added.

7. Show how the beliefs change when you change the priors for a root node

(rather than adding evidence).