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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.

✔ Author(s):
✔ Title: Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs, Series Number 6)
✔ Rating : 4.8 out of 5 base on (32 reviews)
✔ ISBN-10: 1108823416
✔ ISBN-13: 9781108823418
✔ Language: English
✔ Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
✔ Device compatibles: Android, iOS, PC and Amazon Kindle

Readers' opinions about Computer Age Statistical Inference, Student Edition by Bradley Efron

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The depth of character development was astounding. I felt like I knew each person intimately, understanding their hopes, fears, and dreams. It made the story so much more meaningful.
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The rich historical context of the book added a layer of depth that I found fascinating. It was like stepping back in time and experiencing history firsthand.
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The plot was so well-paced that I lost track of time while reading. I was completely immersed in the story, eagerly turning the pages to uncover the next twist.


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