Probability for Statistics and Machine Learning

Fundamentals and Advanced Topics

  • Anirban┬áDasGupta

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Anirban DasGupta
    Pages 1-93
  3. Anirban DasGupta
    Pages 95-121
  4. Anirban DasGupta
    Pages 123-165
  5. Anirban DasGupta
    Pages 167-197
  6. Anirban DasGupta
    Pages 199-219
  7. Anirban DasGupta
    Pages 249-292
  8. Anirban DasGupta
    Pages 293-322
  9. Anirban DasGupta
    Pages 323-338
  10. Anirban DasGupta
    Pages 339-374
  11. Anirban DasGupta
    Pages 375-400
  12. Anirban DasGupta
    Pages 401-436
  13. Anirban DasGupta
    Pages 437-462
  14. Anirban DasGupta
    Pages 505-526
  15. Anirban DasGupta
    Pages 527-558
  16. Anirban DasGupta
    Pages 559-582
  17. Anirban DasGupta
    Pages 613-687
  18. Anirban DasGupta
    Pages 689-746
  19. Back Matter
    Pages 747-782

About this book


This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.

This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.


Asymptotics Boot Strap Machine Learning Markov Chain Monte Carlo Proabability models

Authors and affiliations

  • Anirban┬áDasGupta
    • 1
  1. 1.Dept. Statistics & MathematicsPurdue UniversityWest LafayetteUSA

Bibliographic information

Industry Sectors
Materials & Steel
Health & Hospitals
Finance, Business & Banking
IT & Software
Consumer Packaged Goods
Energy, Utilities & Environment
Oil, Gas & Geosciences