Mark Richard Dufour Ibrahim Alameddine Song S. Qian 2022 <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.<span>&nbsp;</span><strong>Bayesian Applications in Evnironmental and Ecological Studies with R and Stan</strong><span>&nbsp;</span>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> application/pdf 10.1201/9781351018784 en Chapman and Hall/CRC Bayesian applications in environmental and ecological studies with R and Stan book