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商品描述
Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you're a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.
The key features of this indispensable book include:
- A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise.
- 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension.
- A comprehensive guide to Sumio Watanabe's groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians.
- Detailed source programs and Stan codes that will enhance readers' grasp of the mathematical concepts presented.
- A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting.
Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!
商品描述(中文翻譯)
通過深入研究數學邏輯的精髓,掌握機器學習和數據科學的藝術。這本綜合教材專注於廣泛應用的信息準則(WAIC),也被稱為渡邊-赤池信息準則,以及廣泛應用的貝葉斯信息準則(WBIC),也被稱為渡邊貝葉斯信息準則。本書巧妙地引導讀者解決相關的數學問題,同時提供了在R和Stan中進行編程的實踐經驗。無論您是一名希望完善模型選擇過程的數據科學家,還是一名希望探索貝葉斯統計學最新發展的研究人員,這本易於理解的指南都將使您對渡邊貝葉斯理論有牢固的掌握。
這本不可或缺的書籍的主要特點包括:
1. 清晰且自成一體的寫作風格,確保讀者在各種專業水平上都能輕松理解。
2. 100個精心挑選的練習題,並附有主文本中的解答,使讀者能夠有效評估自己的進展和理解程度。
3. 對渡邊純夫開創性的貝葉斯理論的全面指南,揭開了這個曾被認為連經驗豐富的統計學家都難以理解的主題的神秘面紗。
4. 詳細的源代碼和Stan代碼,將增強讀者對所呈現的數學概念的理解。
5. 在第6章中對代數幾何主題的簡化處理,使貝葉斯理論更易於理解,更不會讓人望而生畏。
踏上機器學習和數據科學之旅,使用這本必備教材,立即揭開WAIC和WBIC的全部潛力!
作者簡介
Joe Suzuki is a professor of statistics at Osaka University, Japan.
作者簡介(中文翻譯)
Joe Suzuki 是日本大阪大學的統計學教授。