Foundations of Bayesian Statistics for Data Scientists: With R and Python
暫譯: 數據科學家的貝葉斯統計基礎：使用 R 和 Python
Agresti, Alan, Kateri, Maria, Grove, Ranjini
相關主題
商品描述
This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and master's students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.
The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.
Key Features:
● Uses real world data examples and contains numerous exercises.
● Includes software appendices in R and Python.
● Offers slides, labs, and other materials on the book's website.
Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods.
商品描述(中文翻譯)
這本書概述了貝葉斯方法在應用統計科學中最重要的推論方法。它被設計為一本教科書，適合對貝葉斯統計感興趣的高年級本科生和碩士生，特別是在數據科學、統計學或數學領域的學生。
讀者應該熟悉微積分，並且應該修過一門涵蓋基本概率規則、概率分佈和期望的統計推論課程，以及傳統頻率主義統計的基本概念，包括抽樣分佈、似然函數、基本推論方法（如點估計、信賴區間、顯著性檢定和線性回歸模型）。
主要特點：
● 使用真實世界的數據範例，並包含大量練習題。
● 包含 R 和 Python 的軟體附錄。
● 提供幻燈片、實驗室和其他材料在書籍的網站上。
每一章開始時都會簡要回顧其主題的主要頻率主義方法，然後介紹相應的貝葉斯方法。本書呈現了一些實質性的理論以及方法，因此適合希望理解貝葉斯方法而不僅僅是應用它們的讀者。重點不僅在於呈現統計方法論，還在於展示如何使用現代軟體實現這些方法，強調適當的模擬方法。
作者簡介
Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician of the Year from the American Statistical Association (Chicago chapter).
Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University. She has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
Ranjini Grove is a Teaching Professor in the Department of Statistics at the University of Washington. Since receiving her doctoral degree at Cornell University, she has also held faculty appointments at Brown University and at the University of Florida. In 2022 she was a finalist for a distinguished teaching award at the University of Washington. Since taking a break from academia to be a stay-at-home mom, she has been devoting much energy towards creating an inclusive, learner-centered, and engaging classroom.
Antonietta Mira is a Professor of Statistics at the Università della Svizzera italiana and Insubria University. She is a Fellow of both the International Society for Bayesian Analysis and the Institute of Mathematical Statistics, elected member of the International Statistical Institute, and a recipient of the Savage Award for outstanding doctoral dissertations in Bayesian econometrics and statistics. Her research focuses on Bayesian learning and computing, with a strong interdisciplinary approach. She is also passionate about science communication through books, exhibitions, and mathematical magic conference shows.
作者簡介(中文翻譯)
艾倫·阿格雷斯提（Alan Agresti），佛羅里達大學榮譽退休教授，著有七本書籍，並在35個國家舉辦短期課程。他的獎項包括英國德蒙福特大學的榮譽博士學位，以及美國統計學會（芝加哥分會）頒發的年度統計學家獎。
瑪麗亞·卡特里（Maria Kateri），亞琛工業大學（RWTH Aachen University）統計與數據科學教授。她在教授數據科學、數學、統計學、計算機科學、商業管理和工程等領域的統計課程方面擁有長期經驗。
蘭吉尼·格羅夫（Ranjini Grove）是華盛頓大學統計系的教學教授。自從在康奈爾大學獲得博士學位以來，她還曾在布朗大學和佛羅里達大學擔任教職。2022年，她成為華盛頓大學卓越教學獎的決賽入圍者。自從暫時離開學術界成為全職媽媽以來，她一直致力於創造一個包容性、以學習者為中心且引人入勝的課堂環境。
安東尼塔·米拉（Antonietta Mira）是瑞士意大利大學（Università della Svizzera italiana）和因蘇布里亞大學的統計學教授。她是國際貝葉斯分析學會和數學統計學會的會士，國際統計學會的選舉成員，以及因其在貝葉斯計量經濟學和統計學方面的傑出博士論文而獲得的薩維奇獎（Savage Award）得主。她的研究重點是貝葉斯學習和計算，並採取強烈的跨學科方法。她也熱衷於通過書籍、展覽和數學魔術會議表演進行科學傳播。
