Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies

History distribution matching method for predicting effectiveness of HIV combination therapies

This paper presents an approach that predicts the effectiveness of HIV combination therapies by simultaneously addressing several problems affecting the available HIV clinical data sets: the different treatment backgrounds of the samples, the uneven representation of the levels of therapy experience, the missing treatment history information, the uneven therapy representation and the unbalanced...

Chapter 20 HIERARCHICAL BAYES MODEL

Dealing with sparse data in predicting outcomes of HIV combination therapies

MOTIVATION As there exists no cure or vaccine for the infection with human immunodeficiency virus (HIV), the standard approach to treating HIV patients is to repeatedly administer different combinations of several antiretroviral drugs. Because of the large number of possible drug combinations, manually finding a successful regimen becomes practically impossible. This presents a major challenge ...

Optical proximity correction with hierarchical Bayes model

Optical proximity correction (OPC) is one of the most important techniques in today’s optical lithography-based manufacturing process. Although the most widely used model-based OPC is expected to achieve highly accurate correction, it is also known to be extremely time-consuming. This paper proposes a regression model for OPC using a hierarchical Bayes model (HBM). The goal of the regression mo...

A Hierarchical Bayes Model for Ranked Conjoint Data

This paper investigates the adequacy of the hierarchical Bayes (HB) model for rankbased conjoint data. While recent research has demonstrated the robustness of the HB model compared to traditional estimation methods for rank based conjoint models, we conduct an indepth analysis of the underlying reasons of these findings. Hereby we investigate the fundamental assumptions of rank based conjoint ...