Bayesian Applications in Environmental and Ecological Studies with R and Stan

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.

Format: Hardback
Length: 395 pages
Publication date: 29 August 2022
Publisher: Taylor & Francis Ltd

Observational data plays a pivotal role in modern ecological and environmental sciences, shaping the direction of research and analysis. However, traditional statistical training often falls short in equipping scientists with the necessary skills to handle the complex data analysis tasks they encounter in their work. To address this challenge, Bayesian methods emerge as a powerful and flexible tool for data analysis.

Bayesian methods offer several advantages over traditional statistical approaches. Firstly, they allow for the integration of information from diverse sources, enabling a more comprehensive understanding of the underlying phenomena. By incorporating prior knowledge and assumptions into the modelling process, Bayesian methods can provide more accurate estimates and predictions, even when data is limited or uncertain.

Furthermore, Bayesian methods offer a flexible framework for model formulation, parameter estimation, and model evaluation. They enable scientists to explore a wide range of hypotheses and test them using a rigorous statistical framework. This flexibility allows researchers to adapt their models to specific data sets and research questions, ensuring that the most appropriate statistical approach is employed.

In the context of environmental and ecological studies, Bayesian Applications in Environmental and Ecological Studies with R and Stan provides a comprehensive Bayesian framework for analysing environmental and ecological data. This book offers an accessible overview of Bayesian methods, emphasising the hypothetical deductive process and particularly model formulation. It provides the necessary background material on Bayesian inference and Monte Carlo simulation, enabling readers to understand the theoretical foundations of Bayesian analysis.

The book is divided into several chapters, each focusing on a specific application of Bayesian methods in environmental and ecological studies. Case studies are used throughout to illustrate the practical implications of Bayesian analysis, covering topics such as water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more. Advanced chapters on Bayesian applications, including Bayesian networks and a change point model, provide readers with the tools to tackle complex research questions and develop more sophisticated models.

To facilitate the learning process, the book includes complete code for all examples, along with the data used in the book. This allows readers to follow along with the examples and apply the Bayesian methods to their own data sets. Additionally, the book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This group of people represents diverse subject matter fields, and the potential power and flexibility of Bayesian methods can benefit their research and decision-making processes.

In conclusion, Bayesian methods offer a robust and flexible tool for data analysis in modern ecological and environmental sciences. By integrating information from diverse sources and enabling the exploration of a wide range of hypotheses, Bayesian methods can provide more accurate estimates and predictions, even when data is limited or uncertain. Bayesian Applications in Environmental and Ecological Studies with R and Stan provides a comprehensive Bayesian framework for analysing environmental and ecological data, making it an invaluable resource for graduate students, researchers, and professionals in the field.

Weight: 930g
Dimension: 234 x 156 (mm)
ISBN-13: 9781138497399