loglik_gauss: Gaussian errors, large scale

In outerbase: Outer Product Regression

Gaussian errors, large scale

Description

loglik = new(loglik_gauss, om, terms, y, x)

This is a standard model which has the form

y = \langle φ(x), θ \rangle + \varepsilon, \varepsilon \sim N(0,σ^2)

where φ(x) is the basis, θ is the coefficient vector, \varepsilon is an unseen noise vector. The parameter vector is of length 1 where para = \log(σ). It is a faster (sometimes) version of loglik_std but can only handle diagonal variational inference.

Arguments

om	an outermod instance to be referred to
terms	a matrix of terms, must have as many columns as dims in om
y	a vector of observations
x	a matrix of predictors, must have as many columns as dims in om and the same number of rows as y

Value

no returns, this is a class which contains methods

See Also

base class: lpdf

outerbase documentation built on June 9, 2022, 5:08 p.m.