WAIC and WBIC with Python Stan by Joe Suzuki (.PDF)+

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File Size: 46.2 MB

WAIC and WBIC with Python Stan: 100 Exercises for Building Logic by Joe Suzuki
Requirements: .ePUB, .PDF reader, 46.2 MB
Overview: 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. Embark on your Machine Learning and Data Science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!
Genre: Non-Fiction > Tech & Devices

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