Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

The twenty first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence Big data , 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 This book takes uThe twenty first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence Big data , 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 This book takes us on an exhilarating 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 The distinctly modern approach integrates methodology and algorithms with statistical inference The book ends with speculation on the future direction of statistics and data science.
Computer Age Statistical Inference Algorithms Evidence and Data Science The twenty first century has seen a breathtaking expansion of statistical methodology both in scope and in influence Big data data science and machine learning have become familiar terms in the n

  • Title: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
  • Author: Bradley Efron Trevor Hastie
  • ISBN: 9781107149892
  • Page: 386
  • Format: Hardcover
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    1 thought on “Computer Age Statistical Inference: Algorithms, Evidence, and Data Science”

    1. tl;dr - medium grain statistical review from Classic (frequentist, Bayesian) to Modern (Monte Carlo, support vector machines)I may never actually finish reading this book. That's okay because I don't think this is the kind of book you just read. I and several folk I work with are making our way through it, using data from their website with the figures as targeted examples for our R scripts. It's a phenominal resource for showing the flow of thinking about statistics, from the 'strictly pen and [...]

    2. Brings forward an amazing clarity, its an experience worth going through. (Of course, given that I am an extremely mediocre person, using Google helped me get the most out of this book).

    3. Two experts from Stanford have written a great historical/philosophical/mathematical overview of modern statistics that compares and contrasts frequentist, Bayesian and computer intensive algorithmic approaches to data analysis. I've fooled with all this stuff, but it's a pleasure having professors this smart tie it all together. It seems like it must have been an Herculean task. There are good examples and a very small amount of R code. The book, from Cambridge U press, is also very well produc [...]

    4. This is a little like Larry Wasserman's books in breadth.

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