VectorGaussianMoments
VectorGaussianMoments()
VectorGaussianMoments(Int32)
VectorGaussianMoments(VectorGaussianMoments)
VectorGaussianMoments(Double, Double)
VectorGaussianMoments(Vector, PositiveDefiniteMatrix)
Dimension
IsPointMass
Mean
Point
Variance
Clone()
Copy(VectorGaussianMoments)
Equals(Object)
FromMeanAndPrecision(Double, Double)
FromMeanAndPrecision(Vector, PositiveDefiniteMatrix)
FromMeanAndVariance(Double, Double)
FromMeanAndVariance(Vector, PositiveDefiniteMatrix)
GetAverageLog(VectorGaussianMoments)
GetHashCode()
GetLogAverageOf(VectorGaussianMoments)
GetLogAverageOfPower(VectorGaussianMoments, Double)
GetLogNormalizer()
GetLogProb(Vector)
GetMarginal(Int32)
GetMarginal(Int32, VectorGaussianMoments)
GetMean()
GetMean(Vector)
GetMeanAndPrecision(Vector, PositiveDefiniteMatrix)
GetMeanAndVariance(Vector, PositiveDefiniteMatrix)
GetMode()
GetVariance()
GetVariance(PositiveDefiniteMatrix)
IsProper()
IsUniform()
MaxDiff(Object)
PointMass(Double)
PointMass(Vector)
Sample()
Sample(Vector)
SetMeanAndPrecision(Vector, PositiveDefiniteMatrix)
SetMeanAndVariance(Vector, PositiveDefiniteMatrix)
SetNatural(Vector, PositiveDefiniteMatrix)
SetTo(VectorGaussianMoments)
SetToPointMass()
SetToPower(VectorGaussianMoments, Double)
SetToProduct(VectorGaussianMoments, VectorGaussianMoments)
SetToProduct(VectorGaussianMoments, IList<Gaussian>)
SetToRatio(VectorGaussianMoments, VectorGaussianMoments, Boolean)
SetToSum(Double, VectorGaussianMoments, Double, VectorGaussianMoments)
SetToUniform()
ToString()
Uniform(Int32)
WeightedSum<T>(T, Int32, Double, T, Double, T)
operator *(VectorGaussianMoments, VectorGaussianMoments)
operator /(VectorGaussianMoments, VectorGaussianMoments)
operator ^(VectorGaussianMoments, Double)
net462
namespace Microsoft.ML.Probabilistic.Distributions
{
[DataContract]
[Quality(QualityBand.Mature)]
public class VectorGaussianMoments : CanGetAverageLog<VectorGaussianMoments>, CanGetLogAverageOf<VectorGaussianMoments>, CanGetLogAverageOfPower<VectorGaussianMoments>, CanGetLogNormalizer, CanGetLogProb<Vector>, CanGetMean<DenseVector>, CanGetMeanAndVariance<Vector, PositiveDefiniteMatrix>, CanGetMode<DenseVector>, CanGetVariance<PositiveDefiniteMatrix>, CanSetMeanAndVariance<Vector, PositiveDefiniteMatrix>, HasPoint<Vector>, IDistribution, IDistribution<Vector>, Sampleable<Vector>, SettableToUniform, Diffable, SettableTo<VectorGaussianMoments>, SettableToPower<VectorGaussianMoments>, SettableToProduct<VectorGaussianMoments>, SettableToProduct<VectorGaussianMoments, VectorGaussianMoments>, SettableToRatio<VectorGaussianMoments>, SettableToRatio<VectorGaussianMoments, VectorGaussianMoments>, SettableToWeightedSum<VectorGaussianMoments>, ICloneable
}
| .NET | 5.06.07.08.09.010.011.0 |
|---|---|
| .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 net462 | |
| Assembly | Microsoft.ML.Probabilistic , Version=0.4.2504.701, PublicKeyToken=e4813102a62778da |
| Referencing | Your project needs a package reference to |
| Package | Microsoft.ML.Probabilistic (0.4.2504.701) netstandard2.0 |
| Platform Restrictions | This framework does not have platform annotations. |
- Built-in API
- Package-provided API