<p><i>Bayesian Data Analysis in Ecology Using Linear Models</i> <i>with R, BUGS, and STAN </i>examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that g
Bayesian data analysis in ecology using linear models with R, BUGS and Stan
✍ Scribed by Almasi, Bettina;Felten, Stefanie von;Guélat, Jérôme;Korner-Nievergelt, Fränzi;Korner-Nievergelt, Pius;Roth, Tobias
- Publisher
- Elsevier;Academic Press
- Year
- 2016
- Tongue
- English
- Leaves
- 329
- Category
- Library
No coin nor oath required. For personal study only.
✦ Subjects
Ekologia--metody statystyczne;R (język programowania);Statystyka Bayesa;Ekologia -- metody statystyczne
📜 SIMILAR VOLUMES
Contributors: Franzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, Pius Korner-Nievergelt <p><i>Bayesian Data Analysis in Ecology Using Linear Models</i> <i>with R, BUGS, and STAN </i>examines the Bayesian and frequentist methods of conducting data analyses.
<p><i>Bayesian Data Analysis in Ecology Using Linear Models</i><i>with R, BUGS, and STAN </i>examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that ge
<P>Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the
<P>Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the
<P>Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the