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_223
100 1 _aSuzuki, Joe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aWAIC and WBIC with Python Stan
_h[electronic resource] :
_b100 Exercises for Building Logic /
_cby Joe Suzuki.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXII, 242 p. 44 illus., 38 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
505 0 _aOver view of Watanabe's Bayes -- Introduction to Watanabe Bayesian Theory -- MCMC and Stan -- Mathematical Preparation -- Regular Statistical Models -- Information Criteria -- Algebraic Geometry -- The Essence of WAOIC -- WBIC and Its Application to Machine Learning.
520 _aMaster 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 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. 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!
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aStatistical Learning.
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819938407
776 0 8 _iPrinted edition:
_z9789819938421
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-99-3841-4
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c263600
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