gaussian_tcch_log_likelihood: Log likelihood function for Gaussian regression with...

In FBMS: Flexible Bayesian Model Selection and Model Averaging

Log likelihood function for Gaussian regression with parameter priors from BAS package

Description

This function computes the marginal likelihood of a Gaussian regression model under different priors.

Usage

gaussian_tcch_log_likelihood(
  y,
  x,
  model,
  complex,
  mlpost_params = list(r = exp(-0.5), beta_prior = list(type = "intrinsic"))
)

Arguments

y	A numeric vector containing the dependent variable.
x	A matrix containing the independent variables, including an intercept column.
model	A logical vector indicating which variables to include in the model.
complex	A list containing complexity measures for the features.
mlpost_params	A list of parameters for the log likelihood, specifying the tuning parameters of beta priors.

Value

A list with elements:

crit	Log marginal likelihood combined with the log prior.
coefs	Posterior mode of the coefficients.

Examples

gaussian_tcch_log_likelihood(rnorm(100), matrix(rnorm(100)), c(TRUE), list(oc=1))

FBMS documentation built on Sept. 13, 2025, 1:09 a.m.