he use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands-on treatment of real-world examples, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists successfully introduces the key concepts of statistical analysis and inference with an accessible, easy-to-follow approach.
The book provides case studies illustrating common problems that exist in the natural resource sciences and presents the statistical knowledge and tools needed for a modern treatment of these issues. Subsequent chapter coverage features:
- An introduction to the fundamental concepts of Bayesian statistical analysis, including its historical background, conjugate solutions, Bayesian hypothesis testing and decision-making, and Markov Chain Monte Carlo solutions
- The relevant advantages of using Bayesian statistical analysis, rather than the traditional frequentist approach, to address research problems
- Two alternative strategies-the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC-to model selection and inference
- The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression
- An introduction to mixed-effects modeling in S-Plus(r) and R for analyzing natural resource data sets with varying error structures and dependencies