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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2011.
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| Edición: | 1st ed. 2011. |
| Colección: | Springer Texts in Statistics,
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.