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)
net481
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
{
public double GetLogNormalizer();
}
}
| .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 net481 | |
| 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