void HPRS::draw() {
check_data_model_samplers();
impute_latent_data();
draw_mu_and_sigma_given_beta();
if (redraw_alpha_and_sigma) {
compute_zero_mean_sufficient_statistics();
draw_mu_given_zero_mean_sufficient_statistics();
draw_sigma_given_zero_mean_sufficient_statistics();
}
}
//----------------------------------------------------------------------
void LogitBartPosteriorSampler::draw() {
check_residuals();
// The idea here is that the full data is imputed based on all the
// trees and their current node values.
//
// The residuals are going to have to be recalculated each time
// the data are imputed.
//
// The appropriate complete data likelihood is that
// y[i]=N(eta,sigsq[i]). The update to each leaf uses
// (y[i]-eta[i,-])~N(leaf[i], sigsq[i]), so subtracting eta is a
// must. The subtraction should be done when leaf[i] is drawn.
impute_latent_data();
BartPosteriorSamplerBase::draw();
}
void BLSSS::draw() {
impute_latent_data();
if(allow_model_selection_) draw_model_indicators();
draw_beta();
}
void LSB::draw(){
impute_latent_data();
draw_gamma();
draw_beta_given_gamma();
}
//----------------------------------------------------------------------
void ProbitBartPosteriorSampler::draw() {
check_residuals();
impute_latent_data();
BartPosteriorSamplerBase::draw();
}
void LS::draw(){
impute_latent_data();
draw_beta();
}
//----------------------------------------------------------------------
void PoissonBartPosteriorSampler::draw() {
impute_latent_data();
BartPosteriorSamplerBase::draw();
}
void PRSS::draw() {
impute_latent_data();
sam_.draw_model_indicators(rng(), complete_data_sufficient_statistics());
sam_.draw_beta(rng(), complete_data_sufficient_statistics());
}
