Bayesian Missing Data Problems

EM, Data Augmentation and Noniterative Computation

von Ming T. Tan, Guo-Liang Tian, Kai Wang Ng

348 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.

• Themen: Mathematik und Naturwissenschaften Mathematik Wahrscheinlichkeitsrechnung und Statistik
• ISBN: 9780367385309
• Sprache: Englisch
• Erscheinungsdatum: 04.11.2019
• Größe: 234 x 156 mm
• Verlag: Chapman and Hall/CRC