Probability and Random Processes 3rd ed Signal Processing Stark Wood, Math HC

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Item specifics

Condition
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See all condition definitionsopens in a new window or tab
Seller Notes
“Probability and Random Processes With Applications to Signal Processing, 3rd Edition — Hardcover ...
Country of Origin
United States
Features
hardcover, black & white illustrations, graphs, MATLAB examples,
Signed By
previous owner stamp
Ex Libris
No
Genre
engineering textbook, probability models, applied mathematics, st
Intended Audience
Adults
Personalized
No
Narrative Type
Nonfiction
Literary Movement
engineering mathematics, probability and statistics in computing
Inscribed
No
Signed
Yes
Edition
third ed
Topic
probability theory, random processes, stochastic processes, elect
Personalize
No
Vintage
Yes
Book Title
Probability and Random Processes With Applications to Signal Proc
Era
21st Century, 2010s
Original Language
English
ISBN
9780130200716
Category

About this product

Product Identifiers

Publisher
Prentice Hall PTR
ISBN-10
0130200719
ISBN-13
9780130200716
eBay Product ID (ePID)
1883666

Product Key Features

Number of Pages
699 Pages
Language
English
Publication Name
Probability and Random Processes with Applications to Signal Processing
Publication Year
2001
Subject
Signals & Signal Processing, Probability & Statistics / Stochastic Processes, Probability & Statistics / General, Telecommunications
Type
Textbook
Author
John W. Woods, Henry Stark
Subject Area
Mathematics, Technology & Engineering
Format
Hardcover

Dimensions

Item Height
1.2 in
Item Weight
40.4 Oz
Item Length
9.4 in
Item Width
7.2 in

Additional Product Features

Edition Number
3
Intended Audience
College Audience
LCCN
2001-021253
Dewey Edition
21
Illustrated
Yes
Dewey Decimal
519.2
Table Of Content
(NOTE: Each chapter concludes with a Summary, Problems, and References.) 1. Introduction to Probability. Introduction: Why Study Probability? The Different Kinds of Probability. Misuses, Miscalculations, and Paradoxes in Probability. Sets, Fields, and Events. Axiomatic Definition of Probability. Joint, Conditional, and Total Probabilities; Independence. Bayes' Theorem and Applications. Combinatorics. Bernoulli Trials Binomial and Multinomial Laws. Asymptotic Behavior of the Binomial Law: The Poisson Law. Normal Approximation to the Binomial Law. 2. Random Variables. Introduction. Definition of a Random Variable. Probability Distribution Function. Probability Density Function. Continuous, Discrete and Mixed Random Variables. Conditional and Joint Distributions and Densities. Failure Rates. 3. Functions of Random Variables. Introduction. Solving Problems of the Type Y=g(X). Solving Problems of the Type Z=g(X,Y). Solving Problems of the Type V=g(X,Y), W=h(X,Y). Additional Examples. 4. Expectation and Introduction to Estimation. Expected Value of a Random Variable. Conditional Expectation. Moments. Chebyshev and Schwarz Inequalities. Moment Generating Functions. Chernoff Bound. Characteristic Functions. Estimators for the Mean and Variance of the Normal Law. 5. Random Vectors and Parameter Estimation. Joint Distributions and Densities. Multiple Transformation of Random Variables. Expectation Vectors and Covariance Matrices. Properties of Covariance Matrices. Simultaneous Diagonalization of Two Covariance Matrices and Applications in Pattern Recognition. The Multidimensional Gaussian Law. Characteristic Functions of Random Vectors. Parameter Estimation. Estimation of Vector Means and Covariance Matrices. Maximum Likelihood Estimators. Linear Estimation of Vector Parameters. 6. Random Sequences. Basic Concepts. Basic Principles of Discrete-Time Linear Systems. Random Sequences and Linear Systems. WSS Random Sequence. Markov Random Sequences. Vector Random Sequences and State Equations. Convergence of Random Sequences. Laws of Large Numbers. 7. Random Processes. Basic Definitions. Some Important Random Processes. Continuous-Time Linear Systems with Random Inputs. Some Useful Classification of Random Processes. Wide-Sense Stationary Processes and LSI Systems. Periodic and Cyclostationary Processes. Vector Processes and State Equations. 8. Advanced Topics in Random Processes. Mean-Square (m.s.) Calculus. m-s Stochastic Integrals. m-s Stochastic Differential Equations. Ergodicity. Karhunen-Loéve Expansion. Representation of Bandlimited and Periodic Processes. 9. Applications to Statistical Signal Processing. Estimation of Random Variables. Innovation Sequences and Kalman Filtering. Wiener Filter for Random Sequence. Expectation-Maximization Algorithm. Hidden Markov Models (HMM). Spectral Estimation. Simulated Annealing. Appendices. Appendix A: Review of Relevant Mathematics. Basic Mathematics. Continuous Mathematics. Residue Method for Inverse Fourier Transform. Mathematical Induction A-4. Appendix B: Gamma and Delta Functions. Gamma Function. Dirac Delta Function. Appendix C: Functional Transformations and Jacobians. Introduction. Jacobians for n = 2. Jacobian for General n. Appendix D: Measure and Probability. Introduction and Basic Ideas. Application of Measure Theory to Probability. Appendix E: Sampled Analog Waveforms and Discrete-time Signals.
Synopsis
For courses in Probability and Random Processes. This book is a comprehensive treatment of probability and random processes that, more than any other available source, combines rigor with accessibility . Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences (Chapter 6), continuous-time random processes (Chapter 7), and statistical signal processing (Chapter 9). The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms (Chapters 1,2) and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes (Chapter 8). The 3rd Edition has a large number of new topics, not present in the 2nd Edition, including additional material on basic probability (Appendix B, Section 1.8, Section 1.11), statistics (chi-square and Student-t in Section 2.4, Section 4.1), misuses of probability (Sec. 1.3), and signal processing (all of Chapter 9)., Provides users with an accessible, yet mathematically solid, treatment of probability and random processes. Many computer examples integrated throughout, including random process examples in MATLAB.Includes expanded discussions of fundamental principles, especially basic probability. Includes new problems which deal with applications of basic theory-in such areas as medical imaging, percolation theory in fractals, and generation of random numbers. Several new topics include Failure rates, the Chernoff bound, interval estimation and the Student t-distribution, and power spectral density estimation. Functions of Random Variables is included as a separate chapter. Mean square convergence and introduction of Martingales is covered in the latter half of the book.Provides electrical and computer engineers with a solid treatment of probability and random processes.
LC Classification Number
TK5102.9.S717 2002

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