Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
by Bradley Efron, Trevor Hastie
Publisher: Stanford University 2016
Number of pages: 493
This book takes us on a journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more.
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by David R. Lilja - University of Minnesota
The book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.
by Douglas McNair (ed.) - IntechOpen
Bayesian networks have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis, assets and liabilities management, AI and robotics, transportation systems planning and optimization, etc.
by Joseph B. Kadane - Chapman and Hall/CRC
An accessible, comprehensive guide to the theory of Bayesian statistics, this book presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods.
by David M Diez, et al. - OpenIntro
Statistics is an applied field with a wide range of practical applications. This book is geared to the high school audience and is specifically tailored to be aligned with the AP Statistics curriculum. It is already being used by many high schools.