{"product_id":"prediction-revisited-isbn-9781119895589","title":"Prediction Revisited","description":"\u003cp\u003e\u003cb\u003eA thought-provoking and startlingly insightful reworking of the science of prediction\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003ePrediction Revisited: The Importance of Observation\u003c\/i\u003e, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. \u003c\/p\u003e\u003cp\u003eThe authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. \u003ci\u003ePrediction Revisited also \u003c\/i\u003eoffers: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eClarifications of commonly accepted but less commonly understood notions of statistics\u003c\/li\u003e \u003cli\u003eInsight into the efficacy of traditional prediction models in a variety of fields\u003c\/li\u003e \u003cli\u003eColorful biographical sketches of some of the key prediction scientists throughout history\u003c\/li\u003e \u003cli\u003eMutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e \u003cp\u003eWith its strikingly fresh perspective grounded in scientific rigor, \u003ci\u003ePrediction Revisited \u003c\/i\u003eis sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past. \u003c\/p\u003e\u003cp\u003eTimeline of Innovations ix\u003c\/p\u003e \u003cp\u003eEssential Concepts xi\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRelevance 2\u003c\/p\u003e \u003cp\u003eInformativeness 3\u003c\/p\u003e \u003cp\u003eSimilarity 4\u003c\/p\u003e \u003cp\u003eRoadmap 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Observing Information 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eObserving Information Conceptually 7\u003c\/p\u003e \u003cp\u003eCentral Tendency 8\u003c\/p\u003e \u003cp\u003eSpread 9\u003c\/p\u003e \u003cp\u003eInformation Theory 10\u003c\/p\u003e \u003cp\u003eThe Strong Pull of Normality 14\u003c\/p\u003e \u003cp\u003eA Constant of Convenience 17\u003c\/p\u003e \u003cp\u003eKey Takeaways 18\u003c\/p\u003e \u003cp\u003eObserving Information Mathematically 20\u003c\/p\u003e \u003cp\u003eAverage 20\u003c\/p\u003e \u003cp\u003eSpread 21\u003c\/p\u003e \u003cp\u003eInformation Distance 24\u003c\/p\u003e \u003cp\u003eObserving Information Applied 26\u003c\/p\u003e \u003cp\u003eAppendix 2.1: On the Inflection Point of the Normal Distribution 32\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Co-occurrence 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCo-occurrence Conceptually 41\u003c\/p\u003e \u003cp\u003eCorrelation as an Information-Weighted Average of Co-occurrence 46\u003c\/p\u003e \u003cp\u003ePairs of Pairs 49\u003c\/p\u003e \u003cp\u003eAcross Many Attributes 50\u003c\/p\u003e \u003cp\u003eKey Takeaways 52\u003c\/p\u003e \u003cp\u003eCo-occurrence Mathematically 54\u003c\/p\u003e \u003cp\u003eThe Covariance Matrix 58\u003c\/p\u003e \u003cp\u003eCo-occurrence Applied 59\u003c\/p\u003e \u003cp\u003eReferences 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Relevance 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRelevance Conceptually 67\u003c\/p\u003e \u003cp\u003eInformativeness 68\u003c\/p\u003e \u003cp\u003eSimilarity 72\u003c\/p\u003e \u003cp\u003eRelevance and Prediction 73\u003c\/p\u003e \u003cp\u003eHow Much Have You Regressed? 74\u003c\/p\u003e \u003cp\u003ePartial Sample Regression 76\u003c\/p\u003e \u003cp\u003eAsymmetry 80\u003c\/p\u003e \u003cp\u003eSensitivity 86\u003c\/p\u003e \u003cp\u003eMemory and Bias 87\u003c\/p\u003e \u003cp\u003eKey Takeaways 88\u003c\/p\u003e \u003cp\u003eRelevance Mathematically 90\u003c\/p\u003e \u003cp\u003ePrediction 95\u003c\/p\u003e \u003cp\u003eEquivalence to Linear Regression 97\u003c\/p\u003e \u003cp\u003ePartial Sample Regression 100\u003c\/p\u003e \u003cp\u003eAsymmetry 102\u003c\/p\u003e \u003cp\u003eRelevance Applied 107\u003c\/p\u003e \u003cp\u003eAppendix 4.1: Predicting Binary Outcomes 114\u003c\/p\u003e \u003cp\u003ePredicting Binary Outcomes Conceptually 114\u003c\/p\u003e \u003cp\u003ePredicting Binary Outcomes Mathematically 116\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fit 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFit Conceptually 123\u003c\/p\u003e \u003cp\u003eFailing Gracefully 125\u003c\/p\u003e \u003cp\u003eWhy Fit Varies 126\u003c\/p\u003e \u003cp\u003eAvoiding Bias 129\u003c\/p\u003e \u003cp\u003ePrecision 130\u003c\/p\u003e \u003cp\u003eFocus 133\u003c\/p\u003e \u003cp\u003eKey Takeaways 134\u003c\/p\u003e \u003cp\u003eFit Mathematically 136\u003c\/p\u003e \u003cp\u003eComponents of Fit 138\u003c\/p\u003e \u003cp\u003ePrecision 139\u003c\/p\u003e \u003cp\u003eFit Applied 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Reliability 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReliability Conceptually 149\u003c\/p\u003e \u003cp\u003eKey Takeaways 153\u003c\/p\u003e \u003cp\u003eReliability Mathematically 155\u003c\/p\u003e \u003cp\u003eReliability Applied 163\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Toward Complexity 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eToward Complexity Conceptually 169\u003c\/p\u003e \u003cp\u003eLearning by Example 170\u003c\/p\u003e \u003cp\u003eExpanding on Relevance 171\u003c\/p\u003e \u003cp\u003eKey Takeaways 175\u003c\/p\u003e \u003cp\u003eToward Complexity Mathematically 177\u003c\/p\u003e \u003cp\u003eComplexity Applied 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Foundations of Relevance 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eObservations and Relevance: A Brief Review of the Main Insights 186\u003c\/p\u003e \u003cp\u003eSpread 187\u003c\/p\u003e \u003cp\u003eCo-occurrence 187\u003c\/p\u003e \u003cp\u003eRelevance 188\u003c\/p\u003e \u003cp\u003eAsymmetry 188\u003c\/p\u003e \u003cp\u003eFit and Reliability 189\u003c\/p\u003e \u003cp\u003ePartial Sample Regression and Machine Learning Algorithms 189\u003c\/p\u003e \u003cp\u003eAbraham de Moivre (1667–1754) 190\u003c\/p\u003e \u003cp\u003ePierre-Simon Laplace (1749–1827) 192\u003c\/p\u003e \u003cp\u003eCarl Friedrich Gauss (1777–1853) 193\u003c\/p\u003e \u003cp\u003eFrancis Galton (1822–1911) 195\u003c\/p\u003e \u003cp\u003eKarl Pearson (1857–1936) 197\u003c\/p\u003e \u003cp\u003eRonald Fisher (1890–1962) 199\u003c\/p\u003e \u003cp\u003ePrasanta Chandra Mahalanobis (1893–1972) 200\u003c\/p\u003e \u003cp\u003eClaude Shannon (1916–2001) 202\u003c\/p\u003e \u003cp\u003eReferences 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003eConcluding Thoughts 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePerspective 209\u003c\/p\u003e \u003cp\u003eInsights 210\u003c\/p\u003e \u003cp\u003ePrescriptions 210\u003c\/p\u003e \u003cp\u003eIndex 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMEGAN CZASONIS\u003c\/b\u003e is Managing Director and Head of Portfolio Management Research at State Street Associates.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMARK KRITZMAN \u003c\/b\u003eis a Founding Partner and CEO of Windham Capital Management. He is also a Founding Partner of State Street Associates and teaches a graduate course at the Massachusetts Institute of Technology.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDAVID TURKINGTON \u003c\/b\u003eis Senior Managing Director and Head of State Street Associates.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA thought-provoking and startlingly insightful reimagination of the science of prediction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003ePrediction Revisited: The Importance of Observation,\u003c\/i\u003e a team of renowned finance and risk experts at the top of their game describes a ground-breaking realignment of the connection between past experiences and future outcomes. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it maps out an elegant prediction system based on a novel measure of statistical relevance.  \u003c\/p\u003e\u003cp\u003eDrawing upon information theory and an obscure yet profound mathematical equivalence, the authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions. Additionally, they introduce a new and more nuanced measure of a prediction’s reliability, enabling researchers to fine tune their responses to specific predictions. \u003c\/p\u003e\u003cp\u003e\u003ci\u003ePrediction Revisited \u003c\/i\u003ealso:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIlluminates many commonly accepted but less commonly understood notions of statistics\u003c\/li\u003e \u003cli\u003eReveals several valuable yet previously unrecognized mathematical equivalences \u003c\/li\u003e \u003cli\u003eIncludes colorful biographical sketches of some of the key scientists whose contributions paved the path to relevance-based prediction\u003c\/li\u003e \u003cli\u003eEnables access to the mathematically minded reader as well as those who prefer an intuitive and conceptual discussion of the book’s key ideas\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWith its strikingly fresh perspective grounded in scientific rigor, \u003ci\u003ePrediction Revisited\u003c\/i\u003e is a must-read for anyone who aspires to reach a new level of understanding and mastery of data-driven prediction.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989840281829,"sku":"NP9781119895589","price":29.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119895589.jpg?v=1761785643","url":"https:\/\/k12savings.com\/products\/prediction-revisited-isbn-9781119895589","provider":"K12savings","version":"1.0","type":"link"}