/*
 * Copyright (c) 2012, Regents of the University of California
 * All rights reserved.
 * 
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 *
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 *
 * * Redistributions in binary form must reproduce the above copyright
 *   notice, this list of conditions and the following disclaimer in
 *   the documentation and/or other materials provided with the
 *   distribution.  
 *
 * * Neither the name of the University of California, Berkeley nor
 *   the names of its contributors may be used to endorse or promote
 *   products derived from this software without specific prior 
 *   written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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package blog.bin;

/**
 * A contingent bayes net (CBN) contains a set of random variables V. For each
 * random variable X in V, there is an associated domain dom(X) and decision
 * tree T_X. The decision tree is a binary tree where each node is a predicate
 * on some subset of V. Each leaf of T_X is a probability distribution
 * parametrized by a subset of V. (Summarized from Arora et. al, UAI-10)
 *
 * This class provides an implementation of CBNs. Over the next few weeks, all
 * inference algorithms will be modified to use CBNs.
 *
 * TODO: This class is currently a stub. Fill out.
 *
 * @author bramsundar
 * @date Aug 11, 2012
 */
public class ContingentBayesNet {
}

@@ -1648,17 +1648,16 @@
 e blog.b
-i
 n;%0A%0A/**%0A
@@ -2037,16 +2037,17 @@
 I-10)%0A *
+ 
 %0A * This
@@ -2170,24 +2170,25 @@
 use CBNs.%0A *
+ 
 %0A * TODO: Th
@@ -2225,24 +2225,25 @@
 Fill out.%0A *
+ 
 %0A * @author 

fixed the typo in ContingentBayesNet.java