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