Verlag: Cambridge University Press, 2021
ISBN 10: 1108823416 ISBN 13: 9781108823418
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Verlag: Cambridge University Press, 2021
ISBN 10: 1108823416 ISBN 13: 9781108823418
Sprache: Englisch
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ISBN 10: 1108823416 ISBN 13: 9781108823418
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Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108823416 ISBN 13: 9781108823418
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In den WarenkorbPaperback. Zustand: New. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. 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. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108823416 ISBN 13: 9781108823418
Sprache: Englisch
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In den WarenkorbZustand: New. Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algor.
Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108823416 ISBN 13: 9781108823418
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. 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. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.