Package InfoCollection

Examples of InfoCollection.NormalTruth

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   * alarms down. To really check, you have to turn on debug and see if all
   * the ones where there were disagreements are the result of ties by looking
   * at the r values before they were rounded.
   */
  public static boolean test2() {
    NormalTruth truth = NormalTruth.SlippageExample();
    Belief b = new Belief(truth.SamplingPrecision());
    InfoCollection.SamplingRule p1 = new ChIn(6, 1, b);
    InfoCollection.SamplingRule p2 = new InfoCollection.IndependentNormalVarianceKnown.ChIn(
        6, 1);
    int numDisagreements = 0;
    boolean ok = true;
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      value[i] = this.mu[i] + s;
      noiseBeta[i] = 1 / this.mvar[i];
    }

    // returns NormalTruth object
    return new NormalTruth(value, noiseBeta);
  }
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    double[] value = new double[M()];
    double[] sigma = Sigma();
    for (int i = 0; i < M(); i++) {
      value[i] = mu[i] + sigma[i] * rnd.nextGaussian();
    }
    return new NormalTruth(value, noiseBeta);
  }
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   * the measurement decision is supposed to chosen randomly. Then, in
   * CheckEquivalence, I could compare the sets and see if they have non-empty
   * intersection.
   */
  public static boolean test3() {
    NormalTruth truth = NormalTruth.SlippageExample();
    Belief b = new Belief(truth.SamplingPrecision());
    SamplingRule p1, p2;
    p1 = new LL1Simple(b);
    // passing 1 to the variance-known version turns on error checking.
    p2 = new InfoCollection.IndependentNormalVarianceKnown.KG(1);
    int numDisagreements = 0;
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       */
      precision[x] = GammaGen.nextDouble(s, a[x], b[x]);
      mean[x] = mu[x] + StrictMath.sqrt(precision[x])
          * rnd.nextGaussian();
    }
    return new NormalTruth(mean, precision);
  }
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Related Classes of InfoCollection.NormalTruth

  • InfoCollection.CorrelatedNormalVarianceKnown.Belief
  • InfoCollection.IndependentNormalVarianceKnown.Belief
  • InfoCollection.IndependentNormalVarianceUnknown.Belief
  • InfoCollection.IndependentNormalVarianceUnknown.ChIn
  • InfoCollection.IndependentNormalVarianceUnknown.LL1Simple
  • java.util.Random
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