Bayesian Applications in Environmental and Ecological Studies with R and Stan

<P>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. <STRONG>Bayesian Applications in Evnironmental and Ecological Studies with R and Stan</STRONG> provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.</P><P><STRONG>Features:</STRONG></P><UL><LI>An accessible overview of Bayesian methods in environmental and ecological studies</LI><LI>Emphasizes the hypothetical deductive process, particularly model formulation</LI><LI>Necessary background material on Bayesian inference and Monte Carlo simulation</LI><LI>Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more</LI><LI>Advanced chapter on Bayesian applications, including Bayesian networks and a change point model</LI><LI>Complete code for all examples, along with the data used in the book, are available via GitHub</LI></UL><P>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.</P>