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| Titel: | Computational statistics in data science |
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| Mitwirkende: | Piegorsch, Walter W. [HerausgeberIn]  |
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| | Levine, Richard A. [HerausgeberIn] |
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| | Zhang, Hao Helen [HerausgeberIn] |
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| | Lee, Thomas C. M. [HerausgeberIn] |
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| Verf.angabe: | edited by Walter W. Piegorsch (University of Arizona), Richard A. Levine (San Diego State University), Hao Helen Zhang (University of Arizona), Thomas C.M. Lee (University of California-Davis) |
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| Verlagsort: | Hoboken, NJ |
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| Verlag: | Wiley |
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| Jahr: | 2022 |
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| Umfang: | 1 Online-Ressource (xxx, 636 Seiten) |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Description based on publisher supplied metadata and other sources |
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| ISBN: | 978-1-119-56105-7 |
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| Abstract: | Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Preface -- Part I Computational Statistics and Data Science -- 1 Computational Statistics and Data Science in the Twenty‐First Century -- 1 Introduction -- 2 Core Challenges 1-3 -- 2.1 Big N -- 2.2 Big P -- 2.3 Big M -- 3 Model‐Specific Advances -- 3.1 Bayesian Sparse Regression in the Age of Big N and Big P -- 3.1.1 Continuous shrinkage: alleviating big M -- 3.1.2 Conjugate gradient sampler for structured high‐dimensional Gaussians -- 3.2 Phylogenetic Reconstruction -- 4 Core Challenges 4 and 5 -- 4.1 Fast, Flexible, and Friendly Statistical Algo‐Ware -- 4.2 Hardware‐Optimized Inference -- 5 Rise of Data Science -- Acknowledgments -- References -- 2 Statistical Software -- 1 User Development Environments -- 1.1 Extensible Text Editors: Emacs and Vim -- 1.2 Jupyter Notebooks -- 1.3 RStudio and Rmarkdown -- 2 Popular Statistical Software -- 2.1 R -- 2.1.1 Why use R over Python or Minitab? -- 2.1.2 Where can users find R support? -- 2.1.3 How easy is R to develop? -- 2.1.4 What is the downside of R? -- 2.1.5 Summary of R -- 2.2 Python -- 2.3 SAS® -- 2.4 SPSS® -- 3 Noteworthy Statistical Software and Related Tools -- 3.1 BUGS/JAGS -- 3.2 C++ -- 3.3 Microsoft Excel/Spreadsheets -- 3.4 Git -- 3.5 Java -- 3.6 JavaScript, Typescript -- 3.7 Maple -- 3.8 MATLAB, GNU Octave -- 3.9 Minitab® -- 3.10 Workload Managers: SLURM/LSF -- 3.11 SQL -- 3.12 Stata® -- 3.13 Tableau® -- 4 Promising and Emerging Statistical Software -- 4.1 Edward, Pyro, NumPyro, and PyMC3 -- 4.2 Julia -- 4.3 NIMBLE -- 4.4 Scala -- 4.5 Stan -- 5 The Future of Statistical Computing -- 6 Concluding Remarks -- Acknowledgments -- References -- Further Reading -- 3 An Introduction to Deep Learning Methods -- 1 Introduction -- 2 Machine Learning: An Overview -- 2.1 Introduction -- 2.2 Supervised Learning. |
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| URL: | Aggregator: https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6930829 |
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| | Cover: https://swbplus.bsz-bw.de/bsz179654972xcov.jpg |
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| Schlagwörter: | (s)Data Science / (s)Numerisches Verfahren / (s)Monte-Carlo-Simulation / (s)Maschinelles Lernen / (s)Hochdimensionale Daten / (s)Visualisierung / (s)Optimierung / (s)Hochleistungsrechnen |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Bibliogr. Hinweis: | Erscheint auch als : Druck-Ausgabe: Computational statistics in data science. - Hoboken, NJ : Wiley, 2022. - xxx, 636 Seiten |
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| Sach-SW: | Electronic books |
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| K10plus-PPN: | 179654972X |
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| Lokale URL UB: |  Zum Volltext |
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978-1-119-56105-7
Computational statistics in data science / Piegorsch, Walter W. [HerausgeberIn]; 2022 (Online-Ressource)
68927570