LSBP_density: Conditional density of a LSBP model

In tommasorigon/DLSBP: Tractable Bayesian density regression via logit stick-breaking priors

Conditional density of a LSBP model

Description

Evaluate the conditional density f_x(y) of a LSBP model, given the parameters and the covariates.

Usage

LSBP_density(y, X1, X2, beta_mixing, beta_kernel, tau)

Arguments

y	The value at which the conditional density must be evaluated.
X1	A n x p_kernel design matrix for the kernel.
X2	A n x p_mixing design matrix for the stick-breaking weights.
beta_mixing	A H-1 x p_mixing dimensional matrix of coefficients for the linear predictor of the stick-breaking weights.
beta_kernel	A H x p_kernel dimensional matrix of coefficients for the linear predictor of the kernel.
tau	A H dimensional vector of coefficients for the kernel precision.

Details

The function LSBP_density evaluates the conditional density f_x(y). The number of mixture components H is inferred from the dimensions of beta_mixing and beta_kernel.

tommasorigon/DLSBP documentation built on Feb. 28, 2023, 8:50 a.m.