Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB - Tapa blanda

Acerca de los autores

Dr. Marc Kéry is a senior scientist at the Swiss Ornithological Institute, a non-profit NGO with about 200 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the universities of Basel and Zürich, Switzerland. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, USA, he moved into animal population ecology and during the last 25 years has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and applied statistics. He has published more than 150 peer-reviewed journal articles and six textbooks on applied statistical modeling. He has taught more than 60 one-week workshops all over the world to biologists and wildlife managers about the concepts and practice of modern statistical analysis in their fields, something which goes together with his books, which target the same audiences.

De la contraportada

Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB provides an important guide and comparison of the powerful new software packages, such as JAGS, Stan, Nimble, and TMB, that are now widely used in research publications.

In addition, this book is simple and accessible, allowing researchers to carry out and understand statistical modeling, provides a gentle introduction to the most exciting, specialist software with which this "statistical learning from data" is often done in current cutting-edge research along with Bayesian statistics and frequentist statistics with its maximum likelihood estimation method.

Though the examples in Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB will come from ecology and environmental science, the underlying statistical models are widely used by scientists across many disciplines. Thus, this book will be useful for anybody who needs to learn and to quickly become proficient in statistical modeling and in the model-fitting engines covered.