Problem 18 Suppose you have a lot of data and are trying to learn the structure of a Bayesian network that fits this data. Consider two arbitrary Bayesian network designs. One is relatively sparse, whereas the other has many connections between its nodes. Imagine that your data consists of very few samples. Which Bayesian network would you expect to achieve a better Bayesian score? How would this change if there were many samples? Problem 19 How many members are there in the Markov equivalence class represented by the partially directed graph shown below? B D (images/equivalence_class.png) A C