Ming T. Tan

Bayesian Missing Data Problems

EM, Data Augmentation and Noniterative Computation

Buch

This book provides a systematic view of Bayesian methods to be used with missing data problems. The text presents a non-iterative approach that provides either explicit or non-iterative sampling calculation of posteriors. This computation includes exact numerical solutions, a conditional sampling approach via data augmentation, and a non-iterative sampling approach via EM-type algorithms. It illustrates the application of Bayesian analysis to important biostatistics problems and to other real-world applications, including the constrained parameter problem reformulated as a missing data problem.. The text includes S-PLUS/R computer codes to su… Mehr

CHF 146.00

Preise inkl. MwSt. und Versandkosten (Portofrei ab CHF 40.00)

Versandfertig innerhalb 1-3 Werktagen

V112: Lieferbar in ca. 10-20 Arbeitstagen

Produktdetails

Weitere Autoren: Tian, Guo-Liang / Ng, Kai Wang

ISBN: 978-1-4200-7749-0
EAN: 9781420077490
Produktnummer: 20744273
Verlag: Taylor and Francis
Sprache: Englisch
Erscheinungsjahr: 2009
Seitenangabe: 346 S.
Masse: H23.4 cm x B15.6 cm 800 g
Abbildungen: Farb., s/w. Abb.
Gewicht: 800

Über den Autor

Ming T. Tan is Professor of Biostatistics in the Department of Epidemiology and Preventive Medicine at the University of Maryland School of Medicine and Director of the Division of Biostatistics at the University of Maryland Greenebaum Cancer Center.Guo-Liang Tian is Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. Kai Wang Ng is Professor and Head of the Department of Statistics and Actuarial Science at the University of Hong Kong.