Overview
- Focuses on widely applicable information criterion (WAIC) & widely applicable Bayesian information criterion (WBIC)
- Presents 100 carefully selected exercises accompanied by solutions in the main text
- Contains detailed source programs and Stan codes to enhance readers’ grasp of the mathematical concepts presented
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About this book
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!
Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Bibliographic Information
Book Title: WAIC and WBIC with R Stan
Book Subtitle: 100 Exercises for Building Logic
Authors: Joe Suzuki
DOI: https://doi.org/10.1007/978-981-99-3838-4
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Softcover ISBN: 978-981-99-3837-7Published: 25 October 2023
eBook ISBN: 978-981-99-3838-4Published: 24 October 2023
Edition Number: 1
Number of Pages: XII, 239
Number of Illustrations: 6 b/w illustrations, 36 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Statistics and Computing/Statistics Programs, Computational Intelligence, Data Structures and Information Theory