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

By: Material type: TextTextSeries: Chapman & Hall/CRC biostatistics series; 32Publication details: 2010 CRC Press Boca RatonDescription: xviii, 328 pISBN:
  • 9781420077490
Subject(s): DDC classification:
  • 519.542 T2B2
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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Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 33-A / Slot 1677 (2nd Floor, East Wing) General Stacks 519.542 T2B2 (Browse shelf(Opens below)) Available 174458

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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