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Introduction to Hierarchical Bayesian Modeling for Ecological Data (eBook, PDF) - Parent, Eric; Rivot, Etienne
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Making statistical modeling and inference more accessible to ecologists and related scientists, this book gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. Data sets, exercises, and R and WinBUGS codes are available on the authors' website.…mehr

Produktbeschreibung
Making statistical modeling and inference more accessible to ecologists and related scientists, this book gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. Data sets, exercises, and R and WinBUGS codes are available on the authors' website.

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Autorenporträt
eric Parent is head of the Research Laboratory for Risk Management in Environmental Sciences (Team MORSE) and a professor in applied statistics and probabilistic modeling for environmental engineering at the National Institute for Rural Engineering, Water and Forest Management (ENGREF/AgroParisTech) in Paris, France. Dr. Parent's research encompasses Bayesian theory and applications, with special emphasis on environmental systems modeling.

etienne Rivot is a researcher in the Fisheries Ecology Laboratory at Agrocampus Ouest in Rennes, France. Dr. Rivot's research focuses on the application of Bayesian statistical modeling for the analysis of ecological data, inference, and predictions.