Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models: Theory and Case Studies in R and NIMBLE
暫譯: 隱馬可夫模型的捕捉-重捕數據貝葉斯分析：理論與R及NIMBLE案例研究
Gimenez, Olivier
商品描述
Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models: Theory and Case Studies in R and NIMBLE introduces ecologists and statisticians to a powerful and unifying framework for analysing capture-recapture data. Hidden Markov models (HMMs) have become a cornerstone in modern population ecology, offering a flexible way to decompose complex processes such as survival, recruitment, and dispersal into simpler building blocks, while explicitly accounting for the fact that we only observe imperfect data rather than the true underlying states. Combined with Bayesian inference, HMMs provide a natural and transparent approach to handle uncertainty, explore model structures, and draw robust conclusions. This book illustrates how to bring these ideas to life using the R package NIMBLE, a fast-developing environment for building and fitting hierarchical models.
Key features include:
- A clear introduction to the principles of Bayesian statistics, HMMs, and the NIMBLE package
- Step-by-step tutorials showing how to implement a wide range of capture-recapture models for open populations
- Fully reproducible examples with data and R code, following a "learning by doing" philosophy
- Case studies drawn from the ecological literature, illustrating how to apply methods to real-world conservation questions
- Practical guidance on model specification, coding strategies, and interpretation of results
Written in an accessible style, this book is designed for ecologists, wildlife biologists, and conservation scientists who already use R and wish to deepen their modelling toolkit, as well as statisticians interested in ecological applications. Beginners will find a self-contained path into Bayesian capture-recapture modelling, while experienced researchers will discover a flexible framework to extend and adapt to their own data and questions.
商品描述(中文翻譯)
《使用隱藏馬可夫模型的捕獲-重捕數據的貝葉斯分析：理論與 R 和 NIMBLE 的案例研究》向生態學家和統計學家介紹了一個強大且統一的框架，用於分析捕獲-重捕數據。隱藏馬可夫模型（HMMs）已成為現代種群生態學的基石，提供了一種靈活的方法，將生存、招募和擴散等複雜過程分解為更簡單的基本組件，同時明確考慮到我們僅觀察到不完美數據而非真實的潛在狀態。結合貝葉斯推斷，HMMs 提供了一種自然且透明的方法來處理不確定性、探索模型結構並得出穩健的結論。本書說明了如何使用 R 套件 NIMBLE 將這些想法付諸實踐，NIMBLE 是一個快速發展的環境，用於構建和擬合層次模型。
本書的主要特點包括：
- 清晰介紹貝葉斯統計、HMMs 和 NIMBLE 套件的原則
- 逐步教程展示如何為開放種群實施各種捕獲-重捕模型
- 完全可重現的示例，附有數據和 R 代碼，遵循「邊做邊學」的理念
- 來自生態文獻的案例研究，說明如何將方法應用於現實世界的保護問題
- 有關模型規範、編碼策略和結果解釋的實用指導
本書以易於理解的風格撰寫，旨在為已經使用 R 並希望深化其建模工具包的生態學家、野生動物生物學家和保護科學家，以及對生態應用感興趣的統計學家提供指導。初學者將找到一條自成體系的貝葉斯捕獲-重捕建模之路，而經驗豐富的研究人員則會發現一個靈活的框架，以擴展和適應他們自己的數據和問題。
作者簡介
Olivier Gimenez is a Research Director at the French National Centre for Scientific Research (CNRS), based at the Centre for Functional and Evolutionary Ecology (CEFE) in Montpellier. Trained as a statistician, he works at the interface of ecology, statistical modelling, and the social sciences, with a particular interest in human-wildlife interactions and population ecology. He coordinates several interdisciplinary projects focusing on mammals and their interactions with human activities. He is the founder of the Statistical Ecology Research Network (GDR Ecologie Statistique), a national network dedicated to statistical ecology. For more than 15 years, he has been teaching statistics to ecologists - especially Bayesian statistics over the past decade - to master's and PhD students.
作者簡介(中文翻譯)
奧利維耶·吉門茲是法國國家科學研究中心（CNRS）的研究主任，工作於蒙彼利埃的功能與演化生態中心（CEFE）。他受過統計學訓練，專注於生態學、統計建模和社會科學的交叉領域，特別關注人類與野生動物的互動及種群生態學。他協調多個跨學科項目，專注於哺乳動物及其與人類活動的互動。他是統計生態研究網絡（GDR Ecologie Statistique）的創始人，這是一個專注於統計生態學的全國性網絡。在過去的15年中，他一直教授統計學給生態學家，尤其是在過去十年中專注於貝葉斯統計，對碩士和博士生進行教學。
