SquaredExp

Nested types and modules

Type	Description
Prior
SquaredExp	Isotropic squared exponential covariance function also known as the Gaussian kernel or RBF kernel k(x,y) = σ² exp(-1/(2lengthscale²) * (x - y)²) + δ[x=y] σ²_{noise} σ² is the signal variance σ²_{noise} is the noise variance

Type

Description

Prior

SquaredExp

Isotropic squared exponential covariance function also known as the Gaussian kernel or RBF kernel

k(x,y) = σ² exp(-1/(2lengthscale²) * (x - y)²) + δ[x=y] σ²_{noise}

σ² is the signal variance σ²_{noise} is the noise variance

Function or value	Description
`fullGradient data parameters` Signature: data:seq<Observation<float>> -> parameters:float [] -> float<MeasureOne> []
`ofParameters parameters` Signature: parameters:seq<float> -> SquaredExp
`randomWalkProposal density location` Signature: density:Normal [] -> location:float [] -> float []
`transitionProbability (...)` Signature: density:Normal [] -> oldLocation:float [] -> newLocation:float [] -> float