Problem 3 In this problem you will analyze a very simple articially created data set from the

Problem 3 In this problem you will analyze a very simple artiÝcially created data set from the bookWeb site http://www.csse.monash.edu.au/bai.html which was created by a v-structure process  ???? 
 ó i.e., one with three variables of which one is the child of the other two which are themselves not directly related. However, it will be instructive if you also locate a real data set generated by a similar v-structure process and answer the questions for both data sets.

Parameterize the Bayesian network  ???? 
 from the data set in at least two of the following ways:

 using the algorithm for the full CPT, that is, Algorithm 7.1

 using a noisy-or parameterization

 using a classiÝcation tree algorithm, such as J48 (available from the WEKA Web site: http://www.cs.waikato.ac.nz/˜ ml/weka/)

 using an order 1 logit model Compare the results. Which parameterization Ýts the data better? For example, which one gives better classiÝcation accuracy?

Since in answering this last question you have (presumably) used the very same data both to parameterize the network and to test it, a close Ýt to the data may bemore an indication of overÝtting than of predictive accuracy. In order to test a modelís generalization accuracy you can divide the data set into a training set and a test set, using only the former to parameterize it and the latter to test predictive (classiÝcation)

accuracy (see Part II introduction).