Variance (bristlecone)

Variance Module

Functions representing how variance is handled
 within likelihood functions.

Type	Description
VarianceFunction<'sigma, 'x>	A variance function maps expected values to per-point σ

Function or value	Description
`constant sigma` Full Usage: `constant sigma` Parameters: sigma : `IncludedParameter<'x>` Returns: `VarianceFunction<'x, 'x>`	Variance is constant within through time. sigma : `IncludedParameter<'x>` Returns: `VarianceFunction<'x, 'x>`
`exponential sigma0 sigma1` Full Usage: `exponential sigma0 sigma1` Parameters: sigma0 : `IncludedParameter<'u>` sigma1 : `IncludedParameter<'v>` Returns: `VarianceFunction<'u, MeasureProduct<MeasureInverse<'v>, MeasureOne>>`	Variance is exponential to the expected value (σ = σ0 * exp(σ1 * x)), where sigma1 is the baseline variance and sigma2 is the rate of growth in variance per units of expx. sigma0 : `IncludedParameter<'u>` sigma1 : `IncludedParameter<'v>` Returns: `VarianceFunction<'u, MeasureProduct<MeasureInverse<'v>, MeasureOne>>`
`proportional sigma0` Full Usage: `proportional sigma0` Parameters: sigma0 : `IncludedParameter<'sigma>` Returns: `VarianceFunction<'x, MeasureProduct<'x, MeasureProduct<MeasureInverse<'sigma>, MeasureOne>>>`	Variance is proportional to the expected value (σ = σ0 * x). sigma0 : `IncludedParameter<'sigma>` Returns: `VarianceFunction<'x, MeasureProduct<'x, MeasureProduct<MeasureInverse<'sigma>, MeasureOne>>>`