Instead of, or in addition to, the IC algorithm above, implement the PC algorithm, again using the oracle from Chapter 2 (rather than significance tests). Your PC learning algorithm should take as input a list of variables and then, using the oracle supplied with some Bayesian network, discover as much of the Bayesian network structure as possible. It should output the network structure in some convenient ascii format

(not a graph layout, unless you happen to find that easy!). For example, you can generate an alphabetical list of nodes, with their adjacencies listed and the arc types indicated (parent, child or undirected).

Run your algorithm on a set of test networks, including at least the IC Learning example, LearningEg.dne, Cancer Neapolitan.dne, and ALARM.dne. Summarize the results of these experiments. If you have also implemented the IC algorithm, compare the results of the IC with the PC Algorithm.