Probability, random processes, and statistical analysis
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Author
Contributions
- Mark, Brian L. (Brian Lai-bue), 1969- - Contributor
- Turin, William - Contributor
Publication
2011 - Cambridge University Press, Cambridge, England
Language
English
Word Count
203,000 words, Guess
Page Count
812 pages
Identifiers
- Open LibraryOL25094348M
- ISBN-139780521895446
- OCLC Control Number826867423
- OCLC Control Numberprobabilityrando00koba_990
- Library of Congress Control Number2011041741
Classifications
- DDC519.2/2
- LCCQA274.2 .K63 2011
Description
"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"--
Links
Other Editions
- Probability, random processes, and statistical analysis
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