bayes

Compute probabilities using Bayesian networks.

This is intended to be a "toy" implementation for educational purposes. The code is not designed for efficiency and is particularly susceptible to numerical rounding errors.

Usage

Consider the example outlined here. The following code illustrates how to calculate the probability that it is raining, given that the grass is wet.

public class Example {
  private static final double DELTA = 0.000001;

  public void testGrassNetwork() {
    BayesNetwork network = BayesNetwork.builder()
        .add(ConditionalDistribution
            .forVariable("Rain")
            .setProbability(1/5d, "True")
            .setProbability(4/5d, "False")
            .build())
        .add(ConditionalDistribution
            .forVariable("Sprinkler")
            .setParents("Rain")
            .setProbability(2/5d, "True", "False")
            .setProbability(3/5d, "False", "False")
            .setProbability(1/100d, "True", "True")
            .setProbability(99/100d, "False", "True")
            .build())
        .add(ConditionalDistribution
            .forVariable("GrassWet")
            .setParents("Sprinkler", "Rain")
            .setProbability(0d, "True", "False", "False")
            .setProbability(1d, "False", "False", "False")
            .setProbability(4/5d, "True", "False", "True")
            .setProbability(1/5d, "False", "False", "True")
            .setProbability(9/10d, "True", "True", "False")
            .setProbability(1/10d, "False", "True", "False")
            .setProbability(99/100d, "True", "True", "True")
            .setProbability(1/100d, "False", "True", "True")
            .build())
        .build();

    Event queryEvent = varEquals("Rain", "True");
    Event evidence = varEquals("GrassWet", "True");

    double probability =
        network.queryProbabilityWithEvidence(queryEvent, evidence);
    assertEquals(891/2491d, probability, DELTA);
  }
}

Unit tests

Unit test can be found under the test directory.

In particular, examples corresponding to a handful of probability exercises can be found in ExampleTest.java.

Dependencies

This project depends on the following libraries:

• JUnit 3
• Google Guava
• Google AutoValue

Google AutoValue, in particular, requires annotation processing to be enabled during compilation.