1	BNJ 2.03α Beginner Developer Tutorial Chris H. Meyer (revised by William H. Hsu) Kansas State University KDD Laboratory http://www.kddresearch.org http://bndev.sourceforge.net
2	Contents Introduction Inference Tutorial Learning Tutorial Coding the Wizards
3	BNJ 2.0α Tools Offers many new tools in GUI form This lecture will focus on the Inference and Learning Wizards We will also look at components such as evidence, CPT tables, and algorithms behind learning
4	Contents Introduction Inference Tutorial Learning Tutorial Coding the Wizards
5	Starting the Inference Wizard (1) Select Tools à Inference Wizard
6	Starting the Inference Wizard (2)
7	Using the Inference Wizard (1)
8	Using the Inference Wizard (2) L-S Algorithm contains 2 main steps: Creates a tree of cliques (junction tree) from the Bayesian Network Computes probability of cliques, then single-node properties are formed based on probability of cliques
9	Using the Inference Wizard (3)
10	Using the Inference Wizard (4) Variable Elimination Uses confactors and the VE algorithm instead of trees Loop Cutset Finds minimum cutsets of probability in Bayesian Networks and computes probability from the cutsets
11	Using the Inference Wizard (5) Pearl’s Propagation Uses message-passing as data from 1 vertex propagates to all neighbors, then to neighbor’s neighbors, etc… All algorithms are useful in the correct circumstance, but full explanation is not in the scope of this lecture
12	Using the Inference Wizard (6) Approximate Inference Logic Sampling, Forward Sampling, Likelihood Weighting, Self-Importance Sampling, Adaptive-Importance Sampling, Pearl MCMC Method, Chavez MCMC Method Again, all algorithms are useful in the correct circumstance Output your data to chosen file on completion
13	Contents Introduction Inference Tutorial Learning Tutorial Coding the Wizards
14	Starting the Learning Wizard (1)
15	Starting the Learning Wizard (2)
16	Using the Learning Wizard (1) Select your Learning Algorithm K2, Genetic Algorithm Wrapper for K2 (GAWK), Genetic Algorithm on Structure, Greedy Structure Learning, Standard Hill-Climbing, Hill-Climbing with adversarial reweighting, Hill-Climbing with Dirichlet prior, Simulated Annealing, Stochastic structural learning
17	Using the Learning Wizard (2) Depending on user’s desire, different learning algorithms will prove to be more effective Output results to file with desired ordering
18	Contents Introduction Inference Tutorial Learning Tutorial Coding the Wizards
19	Inference Wizard Coding (1) GUI Main GUI window passed as owner of InferenceWizard.java All Buttons, JButtons, JRadioButtons, etc. are added to InferenceWizard.java’s ActionListener(actionEvent e) method
20	Inference Wizard Coding (2) Secondary Windows Built using BNJFileDialogFactory class GUI = First 500 lines of code for InferenceWizard.java Main Brain = Last 100 lines of code
21	Inference Wizard Coding (3) Approximate Inference is slightly more complicated à
22	Inference Wizard Coding (4) JTree, DefaultTreeModel, and InferenceResult are used for inference calculation These are found in javax.swing.tree.* and edu.ksu.cis.bnj.bbn.inference.*
23	Inference Wizard Coding (5) Inference.getMarginals() returns an InferenceResult, which contains keys used for enumeration InferenceWizard parses all nodes, and adds them to a DefaultTreeModel. On completion of parsing, the JTree is updated with this model
24	Inference Wizard Coding (6) JPanel is created upon model completion for results
25	Coding the Learning Wizard (1) GUI Almost exactly the same as InferenceWizard.java, except… Database functionality has been implemented Currently supports Oracle, MySQL, and PostgreSQL
26	Coding the Learning Wizard (2) Except for Database methods, connect() and disconnect(), the Learning Wizard is basically a mirror of the Inference Wizard GUI = First 400 lines of code for LearningWizard.java Main Brain = Last 30 lines of code
27	Coding the Learning Wizard (3) Key element of LearningWizard.java = Learner Imported from edu.ksu.cis.bnj.bbn.learning.* Learner is then instantiated with the selected algorithm and data as parameters i.e. : learner = Learner.load(learningEngines[selectedAlgorithm][1], data); Or learner = new PRMk2((Database) data, null);
28	Coding the Learning Wizard (4) Learned graph is returned via Learner.getGraph() This statement returns a BBNGraph suitable for viewing Check edu.ksu.cis.bnj.bbn.learning.* for algorithm explanations