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Titlebook: Case Studies in Bayesian Statistics; Volume V Constantine Gatsonis,Robert E. Kass,Mike West Conference proceedings 2002 Springer Science+Bu

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書目名稱Case Studies in Bayesian Statistics
副標(biāo)題Volume V
編輯Constantine Gatsonis,Robert E. Kass,Mike West
視頻videohttp://file.papertrans.cn/223/222246/222246.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Case Studies in Bayesian Statistics; Volume V Constantine Gatsonis,Robert E. Kass,Mike West Conference proceedings 2002 Springer Science+Bu
描述The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three invited case studies with the accompanying discussion as well as ten contributed pa- pers selected by a refereeing process. The majority of case studies in the volume come from biomedical research. However, the reader will also find studies in education and public policy, environmental pollution, agricul- ture, and robotics. INVITED PAPERS The three invited cases studies at the workshop discuss problems in ed- ucational policy, clinical trials design, and environmental epidemiology, respectively. 1. In School Choice in NY City: A Bayesian Analysis ofan Imperfect Randomized Experiment J. Barnard, C. Frangakis, J. Hill, and D. Rubin report on the analysis of the data from a randomized study conducted to evaluate the New YorkSchool Choice Scholarship Pro- gram. The focus ofthe paper is on Bayesian methods for addressing the analytic challenges pos
出版日期Conference proceedings 2002
關(guān)鍵詞Markov model; Odds; bayesian statistics; hidden Markov model; modeling; randomized experiment; statistics
版次1
doihttps://doi.org/10.1007/978-1-4613-0035-9
isbn_softcover978-0-387-95169-0
isbn_ebook978-1-4613-0035-9Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media New York 2002
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Estimating Genotype Probabilities in Complex Pedigreescomplete Markov chain Monte Carlo (MCMC) methods, such as the Gibbs sampler, can be used to sample genotypes at the marker and trait loci. The Markov chain that corresponds to the scalar Gibbs sampler may not work due to slow mixing. Further, the Gibbs chain may not be irreducible when sampling geno
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Identifying Carriers of a Genetic Modifier Using Nonparametric Bayesian Methodsch carry the . allele of the . gene, an allele which results in the development of intestinal tumors. Offspring from this cross are genotyped for . and tumor counts are recorded. It is assumed that offspring carrying only . have tumor counts distributed according to .., while offspring having both .
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