This is a base class designed to handle the learning of the underlying coefficients, hyperparameters, and parameters associated with a specific learning instance. Polymorphism allows for the implied methods to be used across several similar classes.
no returns, this is a class which contains methods
lpdf$valcurrent value
lpdf$paracurrent model parameters
lpdf$coeffcurrent coefficients
lpdf$compute_valon calling update, compute value and store in
val
lpdf$gradcurrent gradient with respect to coefficients
lpdf$gradhypcurrent gradient with respect to covariance hyperparameters
lpdf$gradparacurrent gradient with respect to model parameters
lpdf$compute_gradon calling update, compute gradient with
respect to coefficients and store in grad
lpdf$compute_gradhypon calling update, compute gradient
with respect to covariance hyperparameters and store in gradhyp
lpdf$compute_gradparaon calling update, compute gradient
with respect to model parameters and store in gradpara
lpdf$update(coeff)update using new coefficients
lpdf$optcg(tol,epoch)do optimization with respect to coefficients via conjugate gradient
lpdf$optnewton()do optimization via matrix inversion, one Newton step
lpdf$updateom()update based on recent version of outermod
lpdf$updatepara(para)update using new model parameters
lpdf$updateterms(terms)update using new terms
lpdf$hess()returns the hessian with respect to coefficients
lpdf$hessgradhyp()returns gradient of hess() with respect to
covariance hyperparameters
lpdf$hessgradpara()returns the gradient of hess() with respect to
model parameters
lpdf$diaghess()returns the diagonal of the hessian with respect to coefficients
lpdf$diaghessgradhyp()returns the gradient of diaghess() with
respect to covariance hyperparameters
lpdf$diaghessgradpara()returns the gradient of diaghess() with
respect to model parameters
lpdf$paralpdf(para)compute the log-prior on the parameters, useful for fitting
lpdf$paralpdf_grad(para)gradient of paralpdf(para)
container class: lpdfvec
derived classes: loglik_std,
loglik_gauss, loglik_gda,
logpr_gauss