* the Dirichlet Process prior parameters (group counts and concentration parameter).
*/
final int centeringCovDof = 2 + 2;
final Matrix centeringCovPriorMean =
MatrixFactory.getDenseDefault().copyArray(new double[][] { {1000d, 0d}, {0d, 1000d}});
final InverseWishartDistribution centeringCovariancePrior =
new InverseWishartDistribution(centeringCovPriorMean.scale(centeringCovDof
- centeringCovPriorMean.getNumColumns() - 1d), centeringCovDof);
final MultivariateGaussian centeringMeanPrior =
new MultivariateGaussian(VectorFactory.getDenseDefault().copyArray(new double[] {0d, 0d}),
centeringCovariancePrior.getMean());
final double centeringCovDivisor = 0.25d;
final NormalInverseWishartDistribution centeringPrior =
new NormalInverseWishartDistribution(centeringMeanPrior, centeringCovariancePrior,
centeringCovDivisor);
final double dpAlphaPrior = 2d;