Название: WAIC and WBIC with Python Stan: 100 Exercises for Building Logic
Автор: Joe Suzuki
Издательство: Springer
Год: 2023
Страниц: 249
Язык: английский
Формат: pdf (true), epub
Размер: 46.2 MB

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. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python 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.

It is very inconvenient if there is no source code in a Machine Learning book. Furthermore, even if there is a package, without the source code, you cannot improve the algorithm. Sometimes the source is made public on platforms like git, but it may only be available in MATLAB or Python or may not be sufficient. In this book, code is written for most processes, so you can understand what it means even if you don't understand the math.

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!




ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШИМ СЮДА!