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 ... Full description
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Description
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.
More Information
| Author | Ming T. Tan, Guo-Liang Tian, Kai Wang Ng |
|---|---|
| Publisher | CRC Press |
| Release year | 2019 |
| Cover type | Softcover |
| EAN | 9780367385309 |