Prediction sets via parametric and nonparametric Bayes: With applications in pharmaceutical industry

Author(s):
Chen, X.
Published
2021
Publication type
Doctoral Thesis

Abstract

This thesis consists of five chapters on how to construct prediction sets for different types of data and models in a parametric or nonparametric Bayesian paradigm. The motivation of the models comes from applications in pharmaceutical manufacturing and quality control.

Prediction setTolerance regionBayesianNonparametricPharmaceutical manufacturingQuality control
Defense date
2021-01-05
Language
English
Supervisor(s)
Vaart, A.W. van der
Committee member(s)
Eliel, E.R.; Meulman, J.J.; Heuvel, E.R. van den; Goeman, J.J.; Nagler, T.W.
Qualification
Ph.D.
Degree granting institution
Mathematical Institute (MI), Faculty of Science, Leiden University, Leiden
hdl
1887/138823