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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
122,46 € 174,95 €

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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 ... Pilns apraksts

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Aprašymas

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.

Vairāk informācijas

Autors Ming T. Tan, Guo-Liang Tian, Kai Wang Ng
Izdevējs CRC Press
Izlaides gads 2019
Vāka tips Mīkstais vāks
EAN 9780367385309
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122,46 € 174,95 €