Bayesian Models: A Statistical Primer for Ecologists

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

貝葉斯建模已成為生態學研究中不可或缺的工具，因為它以統計一致的方式處理複雜性。這本教科書以生態學家能理解的語言提供了對最新貝葉斯方法的全面且易於理解的介紹。與其他相關書籍不同，本書強調計算背後的原則，讓生態學家對如何實施這種強大的統計方法有整體的理解。

《貝葉斯模型》是非統計學家的基礎讀物。它從概率的定義開始，逐步發展出一系列相關的思想，包括基本分佈理論、網絡圖、階層模型、馬可夫鏈蒙特卡羅和從單一和多個模型進行推論。這本獨特的書籍較少強調電腦編碼，而是簡明扼要地介紹了理解貝葉斯分析的數學統計學知識。它還解釋了如何撰寫正確的階層貝葉斯模型並在計算、研究論文和提案中使用它們。

這本入門書使生態學家能夠理解貝葉斯建模背後的統計原則，並將其應用於研究、教學、政策和管理。

主要特點包括：
- 以非統計學家能理解的語言介紹貝葉斯建模的數學和統計基礎
- 包括基本分佈理論、網絡圖、階層模型、馬可夫鏈蒙特卡羅等內容
- 較少強調電腦編碼，更注重基本原則
- 解釋如何撰寫正確的階層貝葉斯模型的統計表達式