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Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation - Ming T. Tan,Guo-Liang Tian,Kai Wang Ng

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2019-11-04
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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 ... Visas aprašymas

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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.

Daugiau informacijos

Autorius Ming T. Tan, Guo-Liang Tian, Kai Wang Ng
Leidėjas CRC Press
Išleidimo metai 2019
Viršelio tipas Minkšti viršeliai
EAN 9780367385309
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131,59 € 175,45 €