Applied Bayesian Modelling (Hardcover)
暫譯: 應用貝葉斯建模 (精裝版)

Peter Congdon

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商品描述

Description

The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book

· Provides a broad and comprehensive account of applied Bayesian modelling.

· Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.

· Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.

· Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.

The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.

 

 

Table of contents

Preface.

The Basis for, and Advantages of, Bayesian Model Estimation via Repeated Sampling.

Hierarchical Mixture Models.

Regression Models.

Analysis of Multi-Level Data.

Models for Time Series.

Analysis of Panel Data.

Models for Spatial Outcomes and Geographical Association.

Structural Equation and Latent Variable Models.

Survival and Event History Models.

Modelling and Establishing Causal Relations: Epidemiological Methods and Models.

Index.

商品描述(中文翻譯)

**描述**

近年來,貝葉斯統計的使用顯著增長,未來無疑將持續增長。《應用貝葉斯建模》是作者暢銷書《貝葉斯統計建模》的後續作品,專注於貝葉斯技術在社會科學和健康科學等重要主題中的潛在應用。這些應用通過許多實際案例和在 WINBUGS 中的軟體實現來說明,WINBUGS 是一個流行的軟體包,提供簡化且靈活的統計建模方法。本書對每個例子提供詳細的解釋,充分說明每個特定問題的模型選擇。

- 提供了應用貝葉斯建模的廣泛且全面的介紹。
- 描述了各種模型評估方法及貝葉斯先驗規範的靈活性。
- 涵蓋了許多應用領域,包括面板數據模型、結構方程及其他多變量結構模型、空間分析、生存分析和流行病學。
- 提供了在 WINBUGS 中的詳細實作範例,以說明所描述技術的實際應用。所有 WINBUGS 程式均可從 FTP 網站獲得。

本書為參與應用統計分析的廣泛人群提供了良好的貝葉斯建模和數據分析入門,包括來自統計學、健康科學和社會科學的研究人員和學生。豐富的範例使本書成為任何參與統計建模和分析的人的理想參考資料。

**目錄**

前言。

貝葉斯模型估計的基礎及優勢:通過重複抽樣。

層級混合模型。

回歸模型。

多層次數據分析。

時間序列模型。

面板數據分析。

空間結果和地理關聯模型。

結構方程和潛在變量模型。

生存和事件歷史模型。

建模和建立因果關係:流行病學方法和模型。

索引。