/** * {@inheritDoc} * * For standard deviation parameter {@code s}, the variance is {@code s^2}. */ public double getNumericalVariance() { final double s = getStandardDeviation(); return s * s; }
public double getStandardDeviation() { return normalDistribution.getStandardDeviation(); }
/** * {@inheritDoc} * * For standard deviation parameter {@code s}, the variance is {@code s^2}. */ public double getNumericalVariance() { final double s = getStandardDeviation(); return s * s; }
/** * {@inheritDoc} * * For standard deviation parameter {@code s}, the variance is {@code s^2}. */ public double getNumericalVariance() { final double s = getStandardDeviation(); return s * s; }
public double testInformationAt(double theta){ double info = 0.0; for(ItemResponseModel i : irm){ info += i.itemInformationAt(theta); } if(method==EstimationMethod.MAP){ double sd = mapPrior.getStandardDeviation(); info += 1.0/(sd*sd); } return info; }
/** * @param param * mean * @param param2 * standard deviation * @return normal distribution */ NormalDistribution getNormalDistribution(double param, double param2) { if (normal == null || normal.getMean() != param || normal.getStandardDeviation() != param2) { normal = new NormalDistribution(param, param2); } return normal; }
@Test public void testRealDistributionDeserializerWithNormalDistribution() throws Exception { String syntheticOptions = "{\"seed\":12345," + "\"delayDistribution\":{\"type\":\"normal\",\"mean\":100,\"stddev\":50}}"; SyntheticOptions sourceOptions = optionsFromString(syntheticOptions, SyntheticOptions.class); assertEquals( 100, (long) ((NormalDistribution) (sourceOptions.delayDistribution.getDistribution())).getMean()); assertEquals( 50, (long) ((NormalDistribution) (sourceOptions.delayDistribution.getDistribution())) .getStandardDeviation()); }
NormalDistribution na = (NormalDistribution) a; NormalDistribution nb = (NormalDistribution) b; return na.getMean() == nb.getMean() && na.getStandardDeviation() == nb.getStandardDeviation(); } else if (c == ParetoDistribution.class) { ParetoDistribution pa = (ParetoDistribution) a;
NormalDistribution na = (NormalDistribution) a; NormalDistribution nb = (NormalDistribution) b; return na.getMean() == nb.getMean() && na.getStandardDeviation() == nb.getStandardDeviation(); } else if (c == ParetoDistribution.class) { ParetoDistribution pa = (ParetoDistribution) a;
NormalDistribution nd = (NormalDistribution) d; j.writeNumberField("mean", nd.getMean()); j.writeNumberField("stdev", nd.getStandardDeviation()); } else if (c == ParetoDistribution.class) { ParetoDistribution pd = (ParetoDistribution) d;
NormalDistribution nd = (NormalDistribution) d; j.writeNumberField("mean", nd.getMean()); j.writeNumberField("stdev", nd.getStandardDeviation()); } else if (c == ParetoDistribution.class) { ParetoDistribution pd = (ParetoDistribution) d;