public void RandNormalP() { double[] m_init = { 1, 2, 3 }; Vector mt = Vector.FromArray(m_init); double[,] v_init = { { 2, 1, 1 }, { 1, 2, 1 }, { 1, 1, 2 } }; PositiveDefiniteMatrix vt = new PositiveDefiniteMatrix(v_init); PositiveDefiniteMatrix pt = ((PositiveDefiniteMatrix)vt.Clone()).SetToInverse(vt); int d = mt.Count; Vector x = Vector.Zero(d); VectorMeanVarianceAccumulator mva = new VectorMeanVarianceAccumulator(d); for (int i = 0; i < nsamples; i++) { Rand.NormalP(mt, pt, x); mva.Add(x); } Vector m = mva.Mean; PositiveDefiniteMatrix v = mva.Variance; Console.WriteLine(""); Console.WriteLine("Multivariate NormalP"); Console.WriteLine("--------------------"); Console.WriteLine("m = {0}", m); double dError = m.MaxDiff(mt); if (dError > TOLERANCE) { Assert.True(false, String.Format("m: error = {0}", dError)); } Console.WriteLine("v = \n{0}", v); dError = v.MaxDiff(vt); if (dError > TOLERANCE) { Assert.True(false, String.Format("v: error = {0}", dError)); } }