glm_logpost: The conditional and joint log posterior function

In feng-li/movingknots: Efficient Bayesian multivariate surface regression

Description

Details.

Usage

glm_logpost(Y, x, Params, callParam, splineArgs, priorArgs, Params_Transform)

Arguments

Y	NA
x	NA
Params	NA Params$xi: Parmas$K:
callParam	callParam$id: It should be able to obtain conditianl posterior or joint posterior or just likelihood. callParam$subset: If the paramater set is larg, we may only update a subset of them.
splineArgs	"list".
priorArgs	priorArgs$prior_type: priorArgs$n0: priorArgs$S0: priorArgs$mu: priorArgs$M: priorArgs$mu0: priorArgs$Sigma0: priorArgs$ka0: priorArgs$ka.mu0: priorArgs$ka.Sigma0: priorArgs$Sigma.mu0: priorArgs$Sigma.Sigma0:
Params_Transform	NA
ParamsTransArgs	"list"

Value

"scalar".

Author(s)

Feng Li, Department of Statistics, Stockholm University, Sweden.

feng-li/movingknots documentation built on March 30, 2021, 11:58 a.m.