Argthtjtr

I am using Weka Java API. I trained a Bayesnet on an Instances object (data set) with class (label) unspecified.

/**

 * Initialization

 */

Instances data = ...;

BayesNet bn = new EditableBayesNet(data);

SearchAlgorithm learner = new TAN();

SimpleEstimator estimator = new SimpleEstimator();

/**

 * Training

 */

bn.initStructure();

learner.buildStructure(bn, data);

estimator.estimateCPTs(bn);

Suppose the Instances object data has three attributes, A, B and C, and the dependency discovered is B->A, C->B.

The trained Bayesnet object bn is not for classification (I did not specify the class attribute for data), but I just want to calculate the joint probability of Pr(A=x, B=y). How do I get this probability from bn?

As far as I know, the distributionForInstance function of BayesNet may be the closest thing to use. It returns the probability distribution of a given instance (in our case, the instances is (A=x, B=y)). To use it, I could create a new Instance object testDataInstance and set value A=x and B=y, and call distributionForInstance with testDataInstance.

/**

 * Obtain Pr(A="x", B="y")

 */ 

Instance testDataInstance = new SparseInstance(3);

Instances testDataSet = new Instances(

            bn.m_Instances);

testDataSet.clear();

testDataInstance.setValue(testDataSet.attribute("A"), "x");

testDataInstance.setValue(testDataSet.attribute("B"), "y");

testDataSet.add(testDataInstance);

bn.distributionForInstance(testDataSet.firstInstance());

However, to my knowledge, the probability distribution indicates probabilities of all possible values for the class attribute in the bayesnet. As I did not specify a class attribute for data, it is unclear to me what the returned probability distribution means.

The javadoc page for distributionForInstance says that it calculates the class membership probabilities: http://weka.sourceforge.net/doc.dev/weka/classifiers/bayes/BayesNet.html#distributionForInstance-weka.core.Instance-

So, that's not what you want probably. I think you can use the getDistribution(int nTargetNode) or getDistribution(java.lang.String sName) to achieve your answer.

P(A=x, B=y) can be calculated as follows,

P(A=x|B=y) = P(A=x, B=y)/P(B=y), which implies,



P(A=x, B=y) = P(A=x|B=y)*P(B=y)

Here is a pseudocode which illustrates my approach,

double AP = bn.getDistribution("A"); // gives P(A|B) table

double BP = bn.getDistribution("B"); // gives P(B|C) table

double BPy = 0;



// I am assuming x,y to be ints, but if they are not,

// there should be some way of calculating BP[0][y] or AP[y][x]

// BP[0][y] represents P(B=y) and AP[y][x] represents P(A=x|B=y)

for(int i=0;i<BP.length;i++){

    BPy+=BP[0][y];

}

//BPy now contains probability of P(B=y)

System.out.println(AP[y][x]*BPy)