VectorGaussian
VectorGaussian()
VectorGaussian(Int32)
VectorGaussian(VectorGaussian)
VectorGaussian(Double, Double)
VectorGaussian(Vector, PositiveDefiniteMatrix)
Dimension
IsPointMass
MeanTimesPrecision
Point
Precision
Clone()
Copy(VectorGaussian)
Equals(Object)
FromCursors(Vector, PositiveDefiniteMatrix)
FromDerivatives(Vector, Vector, PositiveDefiniteMatrix, Boolean)
FromMeanAndPrecision(Double, Double)
FromMeanAndPrecision(Vector, PositiveDefiniteMatrix)
FromMeanAndVariance(Double, Double)
FromMeanAndVariance(Vector, PositiveDefiniteMatrix)
FromNatural(Vector, PositiveDefiniteMatrix)
GetAverageLog(VectorGaussian)
GetHashCode()
GetLogAverageOf(VectorGaussian)
GetLogAverageOfPower(VectorGaussian, Double)
GetLogNormalizer()
GetLogProb(Vector)
GetLogProb(Vector, LowerTriangularMatrix, Vector)
GetLogProb(Vector, Vector, PositiveDefiniteMatrix)
GetLogProb(Vector, Vector, PositiveDefiniteMatrix, LowerTriangularMatrix, Vector)
GetLogProbPrep()
GetMarginal(Int32)
GetMarginal(Int32, VectorGaussian)
GetMean()
GetMean(Vector)
GetMean(Vector, PositiveDefiniteMatrix)
GetMeanAndPrecision(Vector, PositiveDefiniteMatrix)
GetMeanAndVariance(Vector, PositiveDefiniteMatrix)
GetMode()
GetVariance()
GetVariance(PositiveDefiniteMatrix)
IsProper()
IsUniform()
MaxDiff(Object)
PointMass(Double)
PointMass(Vector)
Sample()
Sample(Vector)
Sample(Vector, LowerTriangularMatrix)
Sample(Vector, PositiveDefiniteMatrix)
SampleFromMeanAndVariance(Vector, PositiveDefiniteMatrix)
SamplePrep()
SetMeanAndPrecision(Vector, PositiveDefiniteMatrix)
SetMeanAndVariance(Vector, PositiveDefiniteMatrix)
SetNatural(Vector, PositiveDefiniteMatrix)
SetTo(VectorGaussian)
SetToPointMass()
SetToPower(VectorGaussian, Double)
SetToProduct(VectorGaussian, VectorGaussian)
SetToRatio(VectorGaussian, VectorGaussian, Boolean)
SetToSum(Double, VectorGaussian, Double, VectorGaussian)
SetToUniform()
ToString()
Uniform(Int32)
WeightedSum<T>(T, Int32, Double, T, Double, T)
operator *(VectorGaussian, VectorGaussian)
operator /(VectorGaussian, VectorGaussian)
operator ^(VectorGaussian, Double)
xamarinios
namespace Microsoft.ML.Probabilistic.Distributions
{
[DataContract]
[Quality(QualityBand.Mature)]
public class VectorGaussian : CanGetAverageLog<VectorGaussian>, CanGetLogAverageOf<VectorGaussian>, CanGetLogAverageOfPower<VectorGaussian>, CanGetLogNormalizer, CanGetLogProb<Vector>, CanGetLogProbPrep<VectorGaussian, Vector>, CanGetMean<DenseVector>, CanGetMeanAndVariance<Vector, PositiveDefiniteMatrix>, CanGetMode<DenseVector>, CanGetVariance<PositiveDefiniteMatrix>, CanSamplePrep<VectorGaussian, Vector>, CanSetMeanAndVariance<Vector, PositiveDefiniteMatrix>, HasPoint<Vector>, IDistribution, IDistribution<Vector>, Sampleable<Vector>, SettableToUniform, Diffable, SettableTo<VectorGaussian>, SettableToPower<VectorGaussian>, SettableToProduct<VectorGaussian>, SettableToProduct<VectorGaussian, VectorGaussian>, SettableToRatio<VectorGaussian>, SettableToRatio<VectorGaussian, VectorGaussian>, SettableToWeightedSum<VectorGaussian>, ICloneable
{
public void SetToSum(double weight1, VectorGaussian dist1, double weight2, VectorGaussian dist2);
}
}
.NET | 5.06.07.08.09.010.0 |
---|---|
.NET Core | 2.02.12.23.03.1 |
.NET Framework | 4.6.14.6.24.74.7.14.7.24.84.8.1 |
.NET Standard | 2.02.1 |
Information specific to xamarinios | |
Assembly | Microsoft.ML.Probabilistic , Version=0.4.2403.801, PublicKeyToken=e4813102a62778da |
Referencing | Your project needs a package reference to |
Package | Microsoft.ML.Probabilistic (0.4.2403.801) netstandard2.0 |
Platform Restrictions | This framework does not have platform annotations. |
- Built-in API
- Package-provided API