prior_LSBP: Prior specification for the LSBP model

In blindedmanuscript/LSBP: Dependent Logit Stick-Breaking Process

Description

The prior argument is a list which contains the following elements:

Usage

prior_LSBP(p_kernel, p_mixing, b_kernel = rep(0, p_kernel),
  B_kernel = diag(10^6, p_kernel), b_mixing = rep(0, p_mixing),
  B_mixing = diag(10^4, p_mixing), a_tau = 0.1, b_tau = 0.1)

Arguments

p_kernel, p_mixing	The dimension of the design matrices for the kernel component and the mixing component, respectively.
b_kernel	A p_kernel dimensional vector representing the prior mean for the Gaussian kernel coefficients.
B_kernel	A p_kernel x p_kernel matrix representing the prior covariance of the Gaussian kernel coefficients.
b_mixing	A p_mixing dimensional vector containing the prior mean of the Gaussian mixing coefficients
B_mixing	A p_mixing x p_mixing matrix representing the prior covariance of the Gaussian mixing coefficients.
a_tau, b_tau	The hyperparameters of a Gamma prior distribution for the kernel precision.

Value

The function returns a list having the same entries provided as argument. Missing arguments are filled with default values.

Examples

data(cars)
prior  <- prior_LSBP(p_kernel=1, p_mixing=2, a_tau=1.5 ,b_tau=1.5)
fit_em <- LSBP_ECM(dist ~ 1 | speed,data=cars, H=4, prior=prior)

blindedmanuscript/LSBP documentation built on May 13, 2019, 8:23 a.m.