Probability, random processes, and statistical analysis

"Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalit...

Fuld beskrivelse

Saved in:
Bibliografiske detaljer
Hovedforfatter: Kobayashi, Hisashi
Institution som forfatter: ebrary, Inc
Andre forfattere: Mark, Brian L. (Brian Lai-bue), 1969-, Turin, William
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cambridge ; New York : Cambridge University Press, 2012.
Fag:
Online adgang:An electronic book accessible through the World Wide Web; click to view
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
Indholdsfortegnelse:
  • Machine generated contents note: 1. Introduction; Part I. Probability, Random Variables and Statistics: 2. Probability; 3. Discrete random variables; 4. Continuous random variables; 5. Functions of random variables and their distributions; 6. Fundamentals of statistical analysis; 7. Distributions derived from the normal distribution; Part II. Transform Methods, Bounds and Limits: 8. Moment generating function and characteristic function; 9. Generating function and Laplace transform; 10. Inequalities, bounds and large deviation approximation; 11. Convergence of a sequence of random variables, and the limit theorems; Part III. Random Processes: 12. Random process; 13. Spectral representation of random processes and time series; 14. Poisson process, birth-death process, and renewal process; 15. Discrete-time Markov chains; 16. Semi-Markov processes and continuous-time Markov chains; 17. Random walk, Brownian motion, diffusion and it's processes; Part IV. Statistical Inference: 18. Estimation and decision theory; 19. Estimation algorithms; Part V. Applications and Advanced Topics: 20. Hidden Markov models and applications; 21. Probabilistic models in machine learning; 22. Filtering and prediction of random processes; 23. Queuing and loss models.