Probability for Statistics and Machine Learning Fundamentals and Advanced Topics /

• Κύριος συγγραφέας: DasGupta, Anirban (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2011.
• Σειρά: Springer Texts in Statistics,
• Θέματα: Statistics.; Computer simulation.; Bioinformatics.; Probabilities.; Statistical Theory and Methods.; Probability Theory and Stochastic Processes.; Simulation and Modeling.

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...

Πίνακας περιεχομένων:

• 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.