Bayesian core : a practical approach to computational Bayesian statistics / Jean-Michel Marin, Christian P. Robert.
"This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book's Web site, it provides an...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York :
Springer,
[2007]
|
| Series: | Springer texts in statistics.
|
| Subjects: |
| Summary: | "This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book's Web site, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. 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 toward an effective programming of the methods given in the book. While R programs are provided on the book's Web site and R hints are given in the computational sections of the book, Bayesian Core: A Practical Approach to Computational Bayesian Statistics requires no knowledge of the R language, and it can be read and used with any other programming language."--Jacket. |
|---|---|
| Physical Description: | xiii, 255 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references (pages 247-249) and index. |
| ISBN: | 0387389792 9780387389790 |