Record Display for the EPA National Library Catalog


Main Title Bayesian applications in environmental and ecological studies with R and Stan /
Author Qian, Song S.,
Other Authors
Author Title of a Work
DuFour, Mark R.,
Alameddine, Ibrahim,
Publisher CRC Press,
Year Published 2023
OCLC Number 1290380754
ISBN 1138497398; 9781138497399; 9781032290072; 1032290072
Subjects Environmental sciences--Statistical methods ; Bayesian statistical decision theory ; R (Computer program language)
Library Call Number Additional Info Location Last
ELBM  GE45.S73Q25 2023 AWBERC Library/Cincinnati,OH 01/17/2024
Edition First edition.
Collation xix, 395 pages : illustrations ; 24 cm.
Includes bibliographical references and index.
Contents Notes
"Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Environmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods"-- Overview -- Bayesian Inference and Monte Carlo Simulation -- An Overview of Bayesian Inference -- Environmental Monitoring and Assessment -- Normal Response Model -- Population and Community: Count Variables -- Hierarchy Modeling and Aggregation -- Bayesian Applications -- Concluding Remarks.