dposterior: dposterior

In pmgrollemund/bliss: Bayesian Functional Linear Regression with Sparse Step Functions

dposterior

Description

Compute (non-normalized) posterior densities for a given parameter set.

Usage

dposterior(posterior_sample, data, theta = NULL)

Arguments

posterior_sample	a list given by the Bliss_Gibbs_Sampler function.
data	a list containing y a numerical vector, the outcomes. x a list of matrices, the qth matrix contains the observations of the qth functional covariate at time points given by grids.
theta	a matrix or a vector which contains the parameter set.

Details

If the theta is NULL, the posterior density is computed from the MCMC sample given in the posterior_sample.

Value

Return the (log) posterior density, the (log) likelihood and the (log) prior density for the given parameter set.

Examples

data(data1)
data(param1)
# result of res_bliss1<-fit_Bliss(data=data1,param=param1)
data(res_bliss1)
# Compute the posterior density of the MCMC sample :
res_poste <- dposterior(res_bliss1$posterior_sample,data1)

pmgrollemund/bliss documentation built on July 15, 2024, 5:02 p.m.