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This book focuses on Bayesian multivariate inference, which is defined as any inference that involves a multivariate (marginal) posterior distribution and is distinct from frequentist multivariate inference in that that is defined as any analysis with a multivariate response variable. This text features multivariate models, including the mutivariate normal and t distributions, Dirichlet-multinomial and gamma-poisson models, and generalized mixed linear models. Experimental design techniques are also covered and include blocking and repeated measures; experimental planning principles (Bayesian stopping and decision rules in experiments to prove); and risk assessments such as a-priori Type I and Type II error risks in such experiments. Decision analysis is presented in this context as a coherent way to compute stopping boundaries and terminal decisions. Planning principles for experiments include information maximization. Each chapter includes problem sets and computer exercises.

Also in the Wiley Series in Probability and Statistics series (view all)
Hahn, Gerald J. / Doganaksoy, Necip / The Role of Statistics in Business and Industry Book - Paperback	Pearlson, Keri E. / Saunders, Carol Silverman / Managing and Using Information Systems Book - Paperback	Tamhane, Ajit C. / Statistical Analysis of Designed Experiments Book - Hardcover	McLachlan, Geoffrey J. / Krishnan, Thriyambakam / The EM Algorithm And Extensions Book - Hardcover

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