Dòng Nội dung
Bayesian and frequentist regression methods / Jon Wakefield
New York : Springer, 2013
700 p. ; cm.
Ký hiệu phân loại (DDC): 519.536
Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the boo
Tài liệu số: (1)
Bayesian essentials with R /Jean-Michel Marin, Christian P. Robert.
New York : Springer, 2014
xiv, 296 pages :illustrations (some color) ;
Ký hiệu phân loại (DDC): 519.542
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.
Tài liệu số: (1)
Bayesian methods for hackers :probabilistic programming and Bayesian inference /Cameron Davidson-Pilon.
New York : Addison-Wesley, 2016
xvi, 226 pages ; 24 cm
Ký hiệu phân loại (DDC): 006.3
Số bản sách: (2)