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商品描述
Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here.
This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.
商品描述(中文翻譯)
計算技術基於模擬,現在已成為統計學家工具箱中不可或缺的一部分。因此,為統計學家提供對這些方法的實用理解至關重要,而使用模擬來解決統計問題是培養直覺和技能的最佳方式。《使用 R 介紹蒙地卡羅方法》從程式設計師的角度涵蓋了統計模擬中使用的主要工具,解釋了每種模擬技術的 R 實現,並提供輸出以便於理解和比較。雖然本書對模擬方法進行了全面的處理,但與 Robert 和 Casella (2004) 相比,對這些方法的理論證明已大幅減少。同樣,這裡不涵蓋更具探索性和不穩定的解決方案。
本書不需要對 R 程式語言或蒙地卡羅方法的初步接觸,也不需要高級數學背景。雖然許多例子是在貝葉斯框架下設置的,但不需要對貝葉斯統計的高級專業知識。本書涵蓋了基本的隨機生成算法、用於積分和優化的蒙地卡羅技術、收斂診斷、馬可夫鏈蒙地卡羅方法,包括 Metropolis-Hastings 和 Gibbs 算法,以及自適應算法。所有章節均包含練習,所有 R 程式均可作為名為 mcsm 的 R 套件獲得。本書適合對模擬方法有實際興趣但沒有先前接觸的人。它旨在對統計、信號處理、通信工程、控制理論、計量經濟學、金融等領域的學生和從業者有用。程式設計部分逐步介紹,以便任何讀者都能輕鬆理解。