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...
Kaydedildi:
| Yazar: | |
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| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
New York, NY :
Springer New York : Imprint: Springer
[20..].
Cham : Springer Nature |
| Seri Bilgileri: | Springer Texts in Statistics
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| Konular: | |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
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 |
İçindekiler:
- 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.