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)
net6.0-windows
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
{
[IgnoreDataMember]
[XmlIgnore]
public bool IsPointMass { get; }
}
}
.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 net6.0 | |
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 | The property getter is supported on all platforms. |
- Built-in API
- Package-provided API