F# Data
Deedle
R Provider
F# ChartingGaussianProcess<'T>
Constructors
| Constructor | Description |
new(covariance, noiseVariance)
Signature: (covariance:Kernel<'T> * noiseVariance:float option) -> GaussianProcess<'T>
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new(...)
Signature: (covariance:Kernel<'T> * meanFunction:MeanFunction * noiseVariance:float option) -> GaussianProcess<'T>
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Instance members
| Instance member | Description |
Covariance
Signature: Kernel<'T>
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LogLikelihood(data)
Signature: data:seq<Observation<'T>> -> float<MeasureOne>
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Computes posterior log likelihood of a Gaussian process given a set of observed data |
Mean
Signature: MeanFunction
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NoiseVariance
Signature: float option
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PosteriorGaussianProcess(...)
Signature: data:seq<Observation<'T>> -> (newLocations:'T []) -> Vector<float> * Matrix<float>
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Computes mean vector and covariance matrix of a posterior Gaussian process given a set of observed data points and a set of new time points without observed values. |
Predict(data newLocations)
Signature: data:seq<Observation<'T>> -> (newLocations:'T []) -> float [] * float []
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Predictive distribution of a Gaussian process given a set of observations Parameters
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PredictiveLogLikelihood(data x)
Signature: data:seq<Observation<'T>> -> x:Observation<'T> -> float
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Predictive log likelihood of a Gaussian process |
