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 aima.core.probability.bayes.BayesianNetwork

public class PriorSampleTest {


  @Test
  public void testPriorSample_basic() {
    // AIMA3e pg. 530
    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    MockRandomizer r = new MockRandomizer(
        new double[] { 0.5, 0.5, 0.5, 0.5 });


    PriorSample ps = new PriorSample(r);

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Examples of aima.core.probability.bayes.BayesianNetwork


  public static final double DELTA_THRESHOLD = ProbabilityModel.DEFAULT_ROUNDING_THRESHOLD;


  @Test
  public void testLikelihoodWeighting_basic() {
    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(
        ExampleRV.SPRINKLER_RV, Boolean.TRUE) };
    MockRandomizer r = new MockRandomizer(
        new double[] { 0.5, 0.5, 0.5, 0.5 });

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Examples of aima.core.probability.bayes.BayesianNetwork


  @Test
  public void testLikelihoodWeighting_AIMA3e_pg533() {
    // AIMA3e pg. 533
    // <b>P</b>(Rain | Cloudy = true, WetGrass = true)
    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] {
        new AssignmentProposition(ExampleRV.CLOUDY_RV, Boolean.TRUE),
        new AssignmentProposition(ExampleRV.WET_GRASS_RV, Boolean.TRUE) };
    // sample P(Sprinkler | Cloudy = true) = <0.1, 0.9>; suppose

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Examples of aima.core.probability.bayes.BayesianNetwork


  public abstract void setUp();


  @Test
  public void testInferenceOnToothacheCavityCatchNetwork() {
    BayesianNetwork bn = BayesNetExampleFactory
        .constructToothacheCavityCatchNetwork();


    CategoricalDistribution d = bayesInference.ask(
        new RandomVariable[] { ExampleRV.CAVITY_RV },
        new AssignmentProposition[] {}, bn);

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Examples of aima.core.probability.bayes.BayesianNetwork

        ProbabilityModel.DEFAULT_ROUNDING_THRESHOLD);
  }


  @Test
  public void testInferenceOnBurglaryAlarmNetwork() {
    BayesianNetwork bn = BayesNetExampleFactory
        .constructBurglaryAlarmNetwork();


    // AIMA3e. pg. 514
    CategoricalDistribution d = bayesInference
        .ask(new RandomVariable[] { ExampleRV.ALARM_RV },

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Examples of aima.core.probability.bayes.BayesianNetwork

public class GibbsAskTest {
  public static final double DELTA_THRESHOLD = ProbabilityModel.DEFAULT_ROUNDING_THRESHOLD;


  @Test
  public void testGibbsAsk_basic() {
    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(
        ExampleRV.SPRINKLER_RV, Boolean.TRUE) };
    MockRandomizer r = new MockRandomizer(new double[] { 0.5, 0.5, 0.5,
        0.5, 0.5, 0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5 });

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Examples of aima.core.probability.bayes.BayesianNetwork

  public static final double DELTA_THRESHOLD = ProbabilityModel.DEFAULT_ROUNDING_THRESHOLD;


  @Test
  public void testPriorSample_basic() {


    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(
        ExampleRV.SPRINKLER_RV, Boolean.TRUE) };
    MockRandomizer r = new MockRandomizer(new double[] { 0.1 });
    RejectionSampling rs = new RejectionSampling(new PriorSample(r));

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Examples of aima.core.probability.bayes.BayesianNetwork


  @Test
  public void testRejectionSampling_AIMA3e_pg532() {
    // AIMA3e pg. 532


    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(
        ExampleRV.SPRINKLER_RV, Boolean.TRUE) };


    // 400 required as 4 variables and 100 samples planned

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Examples of org.encog.ml.bayesian.BayesianNetwork

  }
  
  public double probabilitySpam(String m) {
    List<String> words = separateSpaces(m);
    
    BayesianNetwork network = new BayesianNetwork();
    BayesianEvent spamEvent = network.createEvent("spam");
    
    int index = 0;
    for( String word: words) {
      BayesianEvent event = network.createEvent(word+index);
      network.createDependency(spamEvent, event);
      index++;
    }
    
    network.finalizeStructure();
    
    //SamplingQuery query = new SamplingQuery(network);
    EnumerationQuery query = new EnumerationQuery(network);
    
    CalcProbability messageProbability = new CalcProbability(this.k);
    messageProbability.addClass(SPAM_DATA.length);
    messageProbability.addClass(HAM_DATA.length);
    double probSpam = messageProbability.calculate(0);


    spamEvent.getTable().addLine(probSpam, true);
    query.defineEventType(spamEvent, EventType.Outcome);
    query.setEventValue(spamEvent, true);
        
    index = 0;
    for( String word: words) {
      String word2 = word+index;
      BayesianEvent event = network.getEvent(word2);
      event.getTable().addLine(this.spamBag.probability(word), true, true); // spam
      event.getTable().addLine(this.hamBag.probability(word), true, false); // ham
      query.defineEventType(event, EventType.Evidence);
      query.setEventValue(event, true);
      index++;

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Examples of org.encog.ml.bayesian.BayesianNetwork


  /**
   * Constructor.
   */
  public BIFHandler() {
    this.network = new BayesianNetwork();
  }

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