Hisashi Kobayashi, Brian L. Mark, William Turin
Probability, Random Processes, and Statistical Analysis
Hisashi Kobayashi, Brian L. Mark, William Turin
Probability, Random Processes, and Statistical Analysis
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Covers the fundamental topics together with advanced theories, including the EM algorithm, hidden Markov models, and queueing and loss systems.
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Covers the fundamental topics together with advanced theories, including the EM algorithm, hidden Markov models, and queueing and loss systems.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 814
- Erscheinungstermin: 15. Dezember 2011
- Englisch
- Abmessung: 250mm x 175mm x 48mm
- Gewicht: 1559g
- ISBN-13: 9780521895446
- ISBN-10: 0521895448
- Artikelnr.: 32558193
- Verlag: Cambridge University Press
- Seitenzahl: 814
- Erscheinungstermin: 15. Dezember 2011
- Englisch
- Abmessung: 250mm x 175mm x 48mm
- Gewicht: 1559g
- ISBN-13: 9780521895446
- ISBN-10: 0521895448
- Artikelnr.: 32558193
Hisashi Kobayashi is the Sherman Fairchild University Professor Emeritus at Princeton University, where he was previously Dean of the School of Engineering and Applied Science. He also spent 15 years at the IBM Research Center, Yorktown Heights, NY, and was the Founding Director of the IBM Tokyo Research Laboratory. He is an IEEE Life Fellow, an IEICE Fellow, was elected to the Engineering Academy of Japan (1992) and received the 2005 Eduard Rhein Technology Award.
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 itô 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.
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 itô 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.
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 itô 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.
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 itô 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.