Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Hardcover)

Ronald Christensen, Wesley Johnson, Adam Branscum, Timothy E Hanson

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商品描述

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data.

The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions.

The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.

Data sets and codes are provided on a supplemental website.

商品描述(中文翻譯)

強調使用WinBUGS和R來分析真實數據,《Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians》提供了解決科學問題的統計工具。它強調統計學的基礎問題,準確預測的重要性,以及科學家和統計學家在分析數據方面的合作需求。提供的WinBUGS代碼為建模和分析各種數據提供了便利的平台。

本書的前五章包含了基本的貝葉斯思想、計算和推斷的核心內容,包括從傳統抽樣模型中建模一個和兩個樣本數據。接下來,本書介紹了馬爾可夫鏈蒙特卡羅(MCMC)模擬等蒙特卡羅方法。在討論回歸中的線性結構後,本書介紹了二項回歸、正態回歸、變異數分析和泊松回歸,然後擴展這些方法以處理相關數據。作者還研究了生存分析和二元診斷測試。有關連續結果的診斷測試的補充章節可在本書的網站上找到。最後一章關於非參數推斷探討了密度估計和平均函數的靈活回歸建模。

適當的統計數據分析需要科學家和統計學家之間的合作。《Bayesian Ideas and Data Analysis》以此方法為例,專注於建模和分析科學數據所需的工具和概念。數據集和代碼可在附加網站上獲得。