文档介绍：Probability, Random Processes, and Statistical Analysis
Together with the fundamentals of probability, random processes, and statistical analy-
sis, 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
• ponent analysis and singular value position
• Autoregressive moving average (ARMA) time series
• Maximum likelihood estimation and the Expectation-Maximization (EM) algorithm
• Brownian motion, geometric Brownian motion, and Ito process
• Black–Scholes differential equation for option pricing
• Hidden Markov model (HMM) and estimation algorithms
• works and sum-product algorithm
• Markov chain Monte Carlo methods
• Wiener and Kalman filters
• Queueing and works
The book will be useful to students and researchers in such areas munications,
signal processing, networks, machine learning, bioinformatics, and 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.
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 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.
Brian L. Mark is a Professor in the Department of Electrical puter Engineer-
ing at e Mason University. Prior to this, he was a research staff member at the
NEC C&C Research Laboratories in Princeton, New Jersey, and in 2002 he received a
National Science Foundation CAREER