Compartir
Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger (en Inglés)
Chen, Ming-Hui ; Müller, Peter ; Sun, Dongchu (Autor)
·
Springer
· Tapa Blanda
Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger (en Inglés) - Chen, Ming-Hui ; Müller, Peter ; Sun, Dongchu
$ 104.20
$ 109.99
Ahorras: $ 5.79
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis ListasSe enviará desde nuestra bodega entre el
Martes 18 de Junio y el
Miércoles 19 de Junio.
Lo recibirás en cualquier lugar de Estados Unidos entre 1 y 3 días hábiles luego del envío.
Reseña del libro "Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger (en Inglés)"
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
