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Prediction revisited : the importance of observation /
"Prediction Revisited is a ground-breaking book for financial analysts and researchers--as well as data scientists in other disciplines--to reconsider classical statistics and approaches to forming predictions. Czasonis, Kritzman, and Turkington lay out the foundations of their cutting-edge app...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2022]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 020 | |a 9781119895602 |q electronic book | ||
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| 050 | 0 | 4 | |a QA76.9.Q36 |b C83 2022 |
| 082 | 0 | 0 | |a 001.4/2 |2 23/eng/20220520 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Czasonis, Megan, |e author. | |
| 245 | 1 | 0 | |a Prediction revisited : |b the importance of observation / |c Megan Czasonis, Mark Kritzman, David Turkington. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2022] | |
| 300 | |a 1 online resource (xvii, 219 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright -- Contents -- Timeline of Innovations -- Essential Concepts -- Preface -- 1 Introduction -- Relevance -- Informativeness -- Similarity -- Roadmap -- 2 Observing Information -- Observing Information Conceptually -- Central Tendency -- Spread -- Information Theory -- The Strong Pull of Normality -- A Constant of Convenience -- Key Takeaways -- Observing Information Mathematically -- Average -- Spread -- Information Distance -- Observing Information Applied -- Appendix 2.1: On the Inflection Point of the Normal Distribution -- References -- 3 Co-occurrence | |
| 505 | 0 | |a Co-occurrence Conceptually -- Correlation as an Information-Weighted Average of Co-occurrence -- Pairs of Pairs -- Across Many Attributes -- Key Takeaways -- Co-occurrence Mathematically -- The Covariance Matrix -- Co-occurrence Applied -- References -- 4 Relevance -- Relevance Conceptually -- Informativeness -- Similarity -- Relevance and Prediction -- How Much Have You Regressed? -- Partial Sample Regression -- Asymmetry -- Sensitivity -- Memory and Bias -- Key Takeaways -- Relevance Mathematically -- Prediction -- Equivalence to Linear Regression -- Partial Sample Regression -- Asymmetry | |
| 505 | 0 | |a Relevance Applied -- Appendix 4.1: Predicting Binary Outcomes -- Predicting Binary Outcomes Conceptually -- Predicting Binary Outcomes Mathematically -- References -- 5 Fit -- Fit Conceptually -- Failing Gracefully -- Why Fit Varies -- Avoiding Bias -- Precision -- Focus -- Key Takeaways -- Fit Mathematically -- Components of Fit -- Precision -- Fit Applied -- 6 Reliability -- Reliability Conceptually -- Key Takeaways -- Reliability Mathematically -- Reliability Applied -- References -- 7 Toward Complexity -- Toward Complexity Conceptually -- Learning by Example -- Expanding on Relevance | |
| 505 | 0 | |a Key Takeaways -- Toward Complexity Mathematically -- Complexity Applied -- References -- 8 Foundations of Relevance -- Observations and Relevance: A Brief Review of the Main Insights -- Spread -- Co-occurrence -- Relevance -- Asymmetry -- Fit and Reliability -- Partial Sample Regression and Machine Learning Algorithms -- Abraham de Moivre (1667-1754) -- Pierre-Simon Laplace (1749-1827) -- Carl Friedrich Gauss (1777-1853) -- Francis Galton (1822-1911) -- Karl Pearson (1857-1936) -- Ronald Fisher (1890-1962) -- Prasanta Chandra Mahalanobis (1893-1972) -- Claude Shannon (1916-2001) -- References | |
| 505 | 0 | |a Concluding Thoughts -- Perspective -- Insights -- Prescriptions -- Index -- EULA | |
| 520 | |a "Prediction Revisited is a ground-breaking book for financial analysts and researchers--as well as data scientists in other disciplines--to reconsider classical statistics and approaches to forming predictions. Czasonis, Kritzman, and Turkington lay out the foundations of their cutting-edge approach to observing information from data. And then characterize patterns between multiple attributes, soon introducing the key concept of relevance. They then show how to use relevance to form predictions, discussing how to measure confidence in predictions by considering the tradeoff between relevance and noise. Prediction Revisited applies this new perspective to evaluate the efficacy of prediction models across many fields and preview the extension of the authors' new statistical approach to machine learning. Along the way they provide colorful biographical sketches of some of the key scientists throughout history who established the theoretical foundation that underpins the authors' notion of relevance--and its importance to prediction. In each chapter, material is presented conceptually, leaning heavily on intuition, and highlighting the key takeaways reframe prediction conceptually. They back it up mathematically and introduce an empirical application of the key concepts to understand. (If you are strongly disinclined toward mathematics, you can pass by the math and concentrate only on the prose, which is sufficient to convey the key concepts of this book.) In fact, you can think of this book as two books: one written in the language of poets and one written in the language of mathematics. Some readers may view the book's key insight about relevance skeptically, because it calls into question notions about statistical analysis that are deeply entrenched in beliefs from earlier training. The authors welcome a groundswell of debate and advancement of thought about prediction."-- |c Provided by publisher | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on October 24, 2022). | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Predictive analytics. | |
| 650 | 0 | |a Business enterprises |x Finance. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Business enterprises |x Finance |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Predictive analytics |2 fast | |
| 700 | 1 | |a Kritzman, Mark P., |e author. | |
| 700 | 1 | |a Turkington, David, |d 1983- |e author. | |
| 776 | 0 | 8 | |i Print version: |a Czasonis, Megan. |t Prediction revisited. |d Hoboken, New Jersey : Wiley, [2022] |z 9781119895589 |w (DLC) 2022017140 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119895589/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH40273681 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL7008415 | ||
| 938 | |a YBP Library Services |b YANK |n 302921228 | ||
| 938 | |a YBP Library Services |b YANK |n 17974678 | ||
| 938 | |a EBSCOhost |b EBSC |n 3300310 | ||
| 994 | |a 92 |b IZTAP | ||