Random Processes for Engineers by Bruce Hajek, Probability Textbook Cambridge HC

First Edition. 2015. Very good condition. Name stamp inside front and rear endpages. Hardcover binding, clean and bright, solid and square. Very light edge, corner and shelf wear. No rips, tears, or stains. Illustrated. From the personal collection of a professor of Engineering & Computer Science. A rigorous yet accessible engineering textbook introducing random processes and probabilistic methods used in modern system analysis and design. Ships from USA, quickly & with care. SYNOPSIS & HISTORY: This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style. Students learn to manage the complexity of randomness through simple classes of random processes, statistical means and correlations, asymptotic analysis, sampling, and effective algorithms. Topics include calculus of random processes in linear systems, Kalman and Wiener filtering, hidden Markov models for statistical inference, the estimation maximization algorithm, and an introduction to martingales and concentration inequalities. Key concepts are reinforced through more than 100 worked examples and 300 thoroughly tested homework problems, half of which are solved in detail at the end of the book.

This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style.

Students learn to manage the complexity of randomness through simple classes of random processes, statistical means and correlations, asymptotic analysis, sampling, and effective algorithms. Topics include calculus of random processes in linear systems, Kalman and Wiener filtering, hidden Markov models for statistical inference, the estimation maximization algorithm, and an introduction to martingales and concentration inequalities.

Key concepts are reinforced through more than 100 worked examples and 300 thoroughly tested homework problems, half of which are solved in detail at the end of the book.

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Engineering & Mathematics, Textbooks, Computer Science

Computer Science, Engineering Textbook, Probability Theory, Random Processes, Stochastic Systems, Engineering Mathematics, Statistical Signal Processing, Kalman Filtering Methods, Markov Models Analysis, Graduate Engineering Course Text, Academic Probability Reference, First Edition Hardcover