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Bayesian missing data problems: EM, data augmentation and noniterative computation
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. Ap...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
CRC Press
2009
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2024634 |
| _version_ | 1780947156825276416 |
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| author | Tan, Ming T Tian, Guo-Liang Ng, Kai Wang |
| author_facet | Tan, Ming T Tian, Guo-Liang Ng, Kai Wang |
| author_sort | Tan, Ming T |
| collection | CERN |
| description | 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. After introducing the missing data problems, Bayesian approach, and poste |
| id | cern-2024634 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2009 |
| publisher | CRC Press |
| record_format | invenio |
| spelling | cern-20246342021-04-21T20:11:29Zhttp://cds.cern.ch/record/2024634engTan, Ming TTian, Guo-LiangNg, Kai WangBayesian missing data problems: EM, data augmentation and noniterative computationMathematical Physics and MathematicsBayesian 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. After introducing the missing data problems, Bayesian approach, and posteCRC Pressoai:cds.cern.ch:20246342009 |
| spellingShingle | Mathematical Physics and Mathematics Tan, Ming T Tian, Guo-Liang Ng, Kai Wang Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title | Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title_full | Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title_fullStr | Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title_full_unstemmed | Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title_short | Bayesian missing data problems: EM, data augmentation and noniterative computation |
| title_sort | bayesian missing data problems: em, data augmentation and noniterative computation |
| topic | Mathematical Physics and Mathematics |
| url | http://cds.cern.ch/record/2024634 |
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