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Probability, random processes, and statistical analysis / Hisashi Kobayashi, Brian L. Mark, William Turin.

By: Contributor(s): Material type: TextTextPublication details: Cambridge, United Kingdom ; New York, United States : Cambridge University Press, 2012.Description: xxxi, 780 p. : charts ; 26 cmISBN:
  • 9780521895446 (hardback)
  • 0521895448 (hardback)
Subject(s): Genre/Form: DDC classification:
  • 519.22 22 KOB
Contents:
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.
Summary: "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, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and It's process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum-Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals"-Summary: "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 not covered in other textbooks. Advanced topics include: - Bayesian inference and conjugate priors-Chernoff bound and large deviation approximation-Principal component analysis and singular value decomposition-Autoregressive moving average (ARMA) time series-Maximum likelihood estimation and the EM algorithm-Brownian motion, geometric Brownian motion, and Ito process-Black-Scholes differential equation for option pricing"-
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Holdings
Item type Current library Collection Call number Vol info Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library First floor Baccah 519.22 KOB (Browse shelf(Opens below)) 21291 Available 000037258
Total holds: 0

Index : p. 759-780.

Bibliography : p. [740]-758.

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.

"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, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and It's process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum-Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals"-

"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 not covered in other textbooks. Advanced topics include: - Bayesian inference and conjugate priors-Chernoff bound and large deviation approximation-Principal component analysis and singular value decomposition-Autoregressive moving average (ARMA) time series-Maximum likelihood estimation and the EM algorithm-Brownian motion, geometric Brownian motion, and Ito process-Black-Scholes differential equation for option pricing"-

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