Examples of aima.core.probability.bayes.exact.EliminationAsk

Package aima.core.probability.bayes.exact

Examples of aima.core.probability.bayes.exact.EliminationAsk

aima.core.probability.bayes.exact.EliminationAsk

Artificial Intelligence A Modern Approach (3rd Edition): Figure 14.11, page 528.

 function ELIMINATION-ASK(X, e, bn) returns a distribution over X inputs: X, the query variable e, observed values for variables E bn, a Bayesian network specifying joint distribution P(X₁, ..., X_n) factors <- [] for each var in ORDER(bn.VARS) do factors <- [MAKE-FACTOR(var, e) | factors] if var is hidden variable the factors <- SUM-OUT(var, factors) return NORMALIZE(POINTWISE-PRODUCT(factors))

Figure 14.11 The variable elimination algorithm for inference in Bayesian networks.

Note: The implementation has been extended to handle queries with multiple variables.
@author Ciaran O'Reilly

            .getX_0_to_X_1().get(sa.getKey()), sa.getValue());
        idx++;
      }
    }


    sensorModel = new FiniteBayesModel(dbn, new EliminationAsk());


    sampleIndexes = new RandVar("SAMPLE_INDEXES", new FiniteIntegerDomain(
        indexes));
  }

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  public static void bayesEliminationAskDemo() {
    System.out.println("DEMO: Bayes Elimination Ask");
    System.out.println("===========================");
    demoToothacheCavityCatchModel(new FiniteBayesModel(
        BayesNetExampleFactory.constructToothacheCavityCatchNetwork(),
        new EliminationAsk()));
    demoBurglaryAlarmModel(new FiniteBayesModel(
        BayesNetExampleFactory.constructBurglaryAlarmNetwork(),
        new EliminationAsk()));
    System.out.println("===========================");
  }

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  //
  // PRIVATE METHODS
  //
  private BayesInference[] getBayesInferenceImplementations() {
    return new BayesInference[] { new EnumerationAsk(),
        new EliminationAsk() };
  }

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 */
public class EliminationAskTest extends BayesianInferenceTest {


  @Before
  public void setUp() {
    bayesInference = new EliminationAsk();
  }

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