• Produktbild: Applied Bayesian Statistics
  • Produktbild: Applied Bayesian Statistics
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Applied Bayesian Statistics With R and OpenBUGS Examples

Fr. 191.00

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.01.2013

Verlag

Springer Us

Seitenzahl

232

Maße (L/B/H)

24.1/16/1.8 cm

Gewicht

524 g

Auflage

2013

Sprache

Englisch

ISBN

978-1-4614-5695-7

Beschreibung

Rezension

From the book reviews:

“Tise textbook is based on the author’s course ‘Bayesian statistics’ and thus it is organised in an incremental manner that, using a variety of practical examples, guides the readers, students and researchers, through the concepts and methodologies required to perform Bayesian analysis. … Each chapter contains examples that underline the important theoretical concepts that are presented and concludes with a selection of problems and exercises. … this book is equally valuable to researchers and lecturers who wish to know more about Bayesian inference.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)

“This book … provides a gentle introduction to both the theory and the ‘nuts and bolts’ of Bayesian analysis. … For those (students in particular) who are looking for a friendly introduction to what is becoming a more popular statistical approach in many areas of science, Applied Bayesian Statistics: With R and OpenBUGS Examples is a very appropriate starting point, one that will give the reader enough understanding and experience to move on to more advanced treatments … .” (Nicole Lazar, Technometrics, Vol. 55 (4), November, 2013)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.01.2013

Verlag

Springer Us

Seitenzahl

232

Maße (L/B/H)

24.1/16/1.8 cm

Gewicht

524 g

Auflage

2013

Sprache

Englisch

ISBN

978-1-4614-5695-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Applied Bayesian Statistics
  • Produktbild: Applied Bayesian Statistics
  • What is Bayesian statistics?.- Review of probability.- Introduction to one-parameter models.- Inference for a population proportion.- Special considerations in Bayesian inference.- Other one-parameter models and their conjugate priors.- More realism please: Introduction to multiparameter models.- Fitting more complex Bayesian models: Markov chain Monte Carlo.- Hierarchical models, and more on convergence assessment.- Regression and hierarchical regression models.- Model Comparison, Model Checking, and Hypothesis Testing.- References.- Index.