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public void ConstructorTest4() { // Create a Wishart distribution with the parameters: WishartDistribution wishart = new WishartDistribution( // Degrees of freedom degreesOfFreedom: 7, // Scale parameter scale: new double[,] { { 4, 1, 1 }, { 1, 2, 2 }, // (must be symmetric and positive definite) { 1, 2, 6 }, } ); // Common measures double[] var = wishart.Variance; // { 224, 56, 504 } double[,] cov = wishart.Covariance; // see below double[,] meanm = wishart.MeanMatrix; // see below // 224 63 175 28 7 7 // cov = 63 56 112 mean = 7 14 14 // 175 112 504 7 14 42 // (the above matrix representations have been transcribed to text using) string scov = cov.ToString(DefaultMatrixFormatProvider.InvariantCulture); string smean = meanm.ToString(DefaultMatrixFormatProvider.InvariantCulture); // For compatibility reasons, .Mean stores a flattened mean matrix double[] mean = wishart.Mean; // { 28, 7, 7, 7, 14, 14, 7, 14, 42 } // Probability density functions double pdf = wishart.ProbabilityDensityFunction(new double[,] { { 8, 3, 1 }, { 3, 7, 1 }, // 0.000000011082455043473361 { 1, 1, 8 }, }); double lpdf = wishart.LogProbabilityDensityFunction(new double[,] { { 8, 3, 1 }, { 3, 7, 1 }, // -18.317902605850534 { 1, 1, 8 }, }); Assert.AreEqual(28.0, mean[0]); Assert.AreEqual(7.0, mean[1]); Assert.AreEqual(7.0, mean[3]); Assert.AreEqual(14.0, mean[4]); Assert.AreEqual(224.0, var[0]); Assert.AreEqual(56.0, var[1]); Assert.AreEqual(504.0, var[2]); Assert.AreEqual(224.0, cov[0, 0]); Assert.AreEqual(63.0, cov[0, 1]); Assert.AreEqual(63.0, cov[1, 0]); Assert.AreEqual(56.0, cov[1, 1]); Assert.AreEqual(0.00000001108245504347336, pdf); Assert.AreEqual(-18.317902605850534, lpdf); }