Deep-Learning-Assisted Statistical Methods with Examples in R
This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. It uniquely demonstrates leveraging deep learning to improve traditional statistical approaches, showcasing their superior performance in pract...
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Chapman and Hall/CRC
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=4346683&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 4346683 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Deep-Learning-Assisted Statistical Methods with Examples in R aug: au: Tianyu Zhan sertl: Chapman & Hall/CRC Data Science Series isbn: 9781041158431 9781041158455 9781003681489 9781040600719 9781040600764 imageinfo: pubinfo: dt: @attributes: year: 2026 month: 01 day: 01 dtAvail: @attributes: year: 2026 month: 03 day: 13 pub: Chapman and Hall/CRC pubContract: CRC Press (Unlimited) place: Boca Raton price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: -1 pda: N printPagesOffline: 60 printPagesOnline: 60 previewPages: 10000 prePubGroup: dewey: @attributes: class: 006.31 item: 006 .31 lc: @attributes: class: Q325.73 item: Q 325 .73 artinfo: ui: 4346683 1568204528 formats: fmt: – @attributes: type: EB doid: NL$4346683$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$4346683$EPUB caption: EPUB download: Y tig: atl: Deep-Learning-Assisted Statistical Methods with Examples in R ptl: Deep-Learning-Assisted Statistical Methods with Examples in R aug: au: Tianyu Zhan su: Deep learning (Machine learning)--Statistical methods Deep learning (Machine learning) sug: subj: MATHEMATICS / Probability & Statistics / Bayesian Analysis COMPUTERS / Artificial Intelligence / General COMPUTERS / Data Science / Neural Networks Deep learning (Machine learning)--Statistical methods Deep learning (Machine learning) ab: This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. It uniquely demonstrates leveraging deep learning to improve traditional statistical approaches, showcasing their superior performance in practical applications. Each topic includes essential background, clear method explanations, and detailed R code demonstrations through case studies. This allows readers to directly apply these methods to their own challenges and easily adapt the underlying principles to related problems.This book delves into statistical inference, introducing advanced strategies for hypothesis testing and point estimation. These innovative methods ingeniously combine both artificial and human intelligence, offering robust solutions for scenarios where traditional optimal analytical solutions are elusive or non-existent. A prime example of their real-world impact is in adaptive clinical trials, where these computational approaches can be readily implemented to optimize trial design and outcomes. The author further explores the multifaceted benefits of deep-learning-assisted statistical methods, extending beyond mere statistical efficiency. It highlights crucial features such as integrity protection, ensuring the trustworthiness of results; computational efficiency, enabling faster and more scalable analyses; and interpretability, which is increasingly vital for transparent communication of complex findings in modern statistics. This section encourages readers to consider a broader spectrum of improvements for new statistical methods, focusing on attributes that enhance their practical utility and societal relevance. Finally, the reader is given a critical examination of the limitations and potential concerns associated with the methods presented in earlier chapters. Crucially, it doesn't just identify these issues but also offers constructive mitigation approaches. This equips readers with essential techniques to safeguard AI-based methodologies with their scientific expertise, ensuring responsible and valid application of these powerful computational tools in diverse scientific and practical domains.This book is a valuable resource for students, practitioners, and researchers integrating statistics and data science techniques to solve impactful real-world problems. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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