Examples of BayesianNetwork

aima.core.probability.bayes.BayesianNetwork
Artificial Intelligence A Modern Approach (3rd Edition): page 510.

Bayesian Networks are used to represent the dependencies among Random Variables. They can represent essentially any full joint probability distribution and in many cases can do so very concisely. A Bayesian network is a directed graph in which each node is annotated with quantitative probability information. The full specification is as follows:

1. Each node corresponds to a random variable, which may be discrete or continuous.

2. A set of directed links or arrows connects pairs of nodes. If there is an arrow from node X to node Y, X is said to be a parent of Y. The graph has no directed cycles (and hence is a directed acyclic graph, or DAG.

3. Each node X_i has a conditional probability distribution P(X_i | Parents(X_i)) that quantifies the effect of the parents on the node.

The topology of the network - the set of nodes and links - specifies the conditional independence relationships that hold in the domain.

A network with both discrete and continuous variables is called a hybrid Bayesian network.

Note(1): "Bayesian Network" is the most common name used, but there are many synonyms, including "belief network", "probabilistic network", "causal network", and "knowledge map". @author Ciaran O'Reilly
org.encog.ml.bayesian.BayesianNetwork
eatonresearch.com/wiki/Bayesian_Network

Examples of org.encog.ml.bayesian.BayesianNetwork

  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
    
  public BayesianNetwork create()
  {
    BayesianNetwork network = new BayesianNetwork();
    BayesianEvent a = network.createEvent("a");
    BayesianEvent b = network.createEvent("b");


    network.createDependency(a, b);
    network.finalizeStructure();
    a.getTable().addLine(0.5, true); // P(A) = 0.5
    b.getTable().addLine(0.2, true, true); // p(b|a) = 0.2
    b.getTable().addLine(0.8, true, false);// p(b|~a) = 0.8    
    network.validate();
    return network;
  }

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Examples of org.encog.ml.bayesian.BayesianNetwork

    Assert.assertEquals(3, network.calculateParameterCount());
  }
  
  public void testPersistEG()
  {
    BayesianNetwork network = create();


    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    BayesianNetwork network2 = (BayesianNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);


    validate(network2);
  }

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Examples of org.encog.ml.bayesian.BayesianNetwork

    validate(network2);
  }
  
  public void testPersistSerial() throws IOException, ClassNotFoundException
  {
    BayesianNetwork network = create();
    
    SerializeObject.save(SERIAL_FILENAME, network);
    BayesianNetwork network2 = (BayesianNetwork)SerializeObject.load(SERIAL_FILENAME);
        
    validate(network2);
  }

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Examples of org.encog.ml.bayesian.BayesianNetwork

   * @param output The output neuron count.
   * @return The new bayesian network.
   */
  public MLMethod create(final String architecture, final int input,
      final int output) {
    BayesianNetwork method = new BayesianNetwork();
    method.setContents(architecture);
    return method;
  }

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