Probability for statistics and machine learning : fundamentals and advanced topics
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 num...
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| Format: | Livre numérique |
| Jezik: | Anglais |
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New York, NY :
Springer New York : Imprint: Springer
[20..].
Cham : Springer Nature |
| Serija: | Springer Texts in Statistics
|
| Teme: | |
| Online pristup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Description d'après consultation du 14 mai 2012 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Probability for statistics and machine learning, fundamentals and advanced topics, Anirban DasGupta, New York, Springer, 2011, 1 vol. (XIX-782 p.), Springer texts in statistics, 978-1-441-99633-6 |
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| 100 | 1 | |a DasGupta, Anirban. | |
| 245 | 1 | 0 | |a Probability for statistics and machine learning : |b fundamentals and advanced topics |c Anirban DasGupta. |
| 260 | |a New York, NY : |b Springer New York : |b Imprint: Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 1 | |a Springer Texts in Statistics |x 1431-875X | |
| 500 | |a Description d'après consultation du 14 mai 2012 | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Notes bibliogr. Index p. 757-781 | ||
| 505 | 1 | |a Chapter 1. Review of Univariate Probability Chapter 2. Multivariate Discrete Distributions Chapter 3. Multidimensional Densities Chapter 4. Advance Distribution Theory Chapter 5. Multivariate Normal and Related Distributions Chapter 6. Finite Sample Theory of Order Statistics and Extremes Chapter 7. Essential Asymptotics and Applications Chapter 8. Characteristic Functions and Applications Chapter 9. Asymptotics of Extremes and Order Statistics Chapter 10. Markov Chains and Applications Chapter 11. Random Walks Chapter 12. Brownian Motion and Gaussian Processes Chapter 13. Posson Processes and Applications Chapter 14. Discrete Time Martingales and Concentration Inequalities Chapter 15. Probability Metrics Chapter 16. Empirical Processes and VC Theory Chapter 17. Large Deviations Chapter 18. The Exponential Family and Statistical Applications Chapter 19. Simulation and Markov Chain Monte Carlo Chapter 20. Useful Tools for Statistics and Machine Learning Appendix A. Symbols, Useful Formulas, and Normal Table. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. | ||
| 650 | |a Probabilités | ||
| 650 | |a Processus stochastiques | ||
| 650 | |a Théorèmes des limites (théorie des probabilités) | ||
| 650 | |a Markov, processus de | ||
| 776 | 0 | |0 157107159 |t Probability for statistics and machine learning |o fundamentals and advanced topics |f Anirban DasGupta |c New York |n Springer |d 2011 |p 1 vol. (XIX-782 p.) |s Springer texts in statistics |z 978-1-441-99633-6 | |
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| 997 | |0 968878 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||