Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)

Richard McElreath

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商品描述

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.

 

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

 

By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

Web Resource
The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

商品描述(中文翻譯)

《統計重新思考:以R和Stan為例的貝葉斯課程》建立讀者對統計建模的知識和信心。考慮到當今基於模型的統計學中即使是較小的編程需求,本書鼓勵讀者進行通常自動化的逐步計算。這種獨特的計算方法確保讀者了解足夠的細節,以在自己的建模工作中做出合理的選擇和解釋。

本書從貝葉斯的角度介紹了廣義線性多層模型,依賴於對貝葉斯概率和最大熵的簡單邏輯解釋。從回歸的基礎到多層模型,本書涵蓋了各個方面。作者還討論了測量誤差、缺失數據以及用於空間和網絡自相關的高斯過程模型。

通過全程使用完整的R代碼示例,本書為進行統計推斷提供了實用的基礎。它旨在為自然科學和社會科學領域的博士生和經驗豐富的專業人士做好更高級或專業化統計建模的準備。

網絡資源
本書附帶了一個R包(rethinking),可在作者的網站和GitHub上獲得。該包的兩個核心函數(map和map2stan)允許從標準模型公式構建各種統計模型。