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Bayesian missing data problems: EM, data augmentation and noniterative computation

By: Tan, Ming T.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC biostatistics series; 32. Publisher: Boca Raton CRC Press 2010Description: xviii, 328 p.ISBN: 9781420077490.Subject(s): Bayesian statistical decision theory | Missing observations (Statistics)DDC classification: 519.542 Summary: Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. (http://www.crcpress.com/product/isbn/9781420077490)
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Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. (http://www.crcpress.com/product/isbn/9781420077490)

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