Statistical Approaches For Hidden Variables In Ecology
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Statistical Approaches for Hidden Variables in Ecology
Author: Nathalie Peyrard
language: en
Publisher: John Wiley & Sons
Release Date: 2022-03-15
The study of ecological systems is often impeded by components that escape perfect observation, such as the trajectories of moving animals or the status of plant seed banks. These hidden components can be efficiently handled with statistical modeling by using hidden variables, which are often called latent variables. Notably, the hidden variables framework enables us to model an underlying interaction structure between variables (including random effects in regression models) and perform data clustering, which are useful tools in the analysis of ecological data. This book provides an introduction to hidden variables in ecology, through recent works on statistical modeling as well as on estimation in models with latent variables. All models are illustrated with ecological examples involving different types of latent variables at different scales of organization, from individuals to ecosystems. Readers have access to the data and R codes to facilitate understanding of the model and to adapt inference tools to their own data.
Introduction to Hierarchical Bayesian Modeling for Ecological Data
Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book 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. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.
Modernizing the Academic Teaching and Research Environment
This book constitutes a valuable manual for young and seasoned business researchers alike, and provides a comprehensive summary for the whole research journey. It is a must-read for all researchers who need to understand the basics of business research, from identifying research topics, to planning and organizing the research process, and selecting the most appropriate methodology for the topic at hand. This book also provides insights on how to avoid common pitfalls in business research and outlines the research skills needed to write a fine piece of research. In order to capture the innovative element of research, the book also highlights methods for thinking outside the box. It also stresses the importance of respecting ethics while conducting business research. Lastly, it presents important cases and provides hands-on training for preparing survey tools. Readers looking to master business research won’t want to miss out on this unique and insightful book.