The Earth's climate system depends entirely on the Sun for its energy. Solar radiation warms the atmosphere and is fundamental to atmospheric composition, while the distribution of solar heating across the planet produces global wind patterns and contributes to the formation of clouds, storms, and rainfall. The Sun's Influence on Climate provides an unparalleled introduction to this vitally important relationship.This accessible primer covers the basic properties of the Earth's climate system, the structure and behavior of the Sun, and the absorption of solar radiation in the atmosphere. It explains how solar activity varies and how these variations affect the Earth's environment, from long-term paleoclimate effects to century timescales in the context of human-induced climate change, and from signals of the 11-year sunspot cycle to the impacts of solar emissions on space weather in our planet's upper atmosphere.Written by two of the leading authorities on the subject, The Sun's Influence on Climate is an essential primer for students and nonspecialists alike.
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.
Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.
This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.
- Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians
- Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more
- Deemphasizes computer coding in favor of basic principles
- Explains how to write out properly factored statistical expressions representing Bayesian models
