Probability, Random Processes, and Statistical Analysis

Probability, Random Processes, and Statistical Analysis

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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 ItAa process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Bauma€“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.Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance Hisashi Kobayashi, Brian L. Mark, William Turin. (c) Generalize the experiment and consider r indistinguishable particles and n distinguishable cells, where n a‰y r. Show that the ... statistics. is found is given by Section 2.3: Bernoulli trials and Bernoullia#39;s theorem 2.10aˆ— Distribution laws and Venn diagram.

Title:Probability, Random Processes, and Statistical Analysis
Author:Hisashi Kobayashi, Brian L. Mark, William Turin
Publisher:Cambridge University Press - 2011-12-15


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