LEADER 05547nam 2200709 a 450 001 9910778937503321 005 20200520144314.0 010 $a1-283-41027-3 010 $a9786613410276 010 $a0-12-387013-5 035 $a(CKB)2550000000084147 035 $a(EBL)858694 035 $a(OCoLC)775872059 035 $a(SSID)ssj0000599792 035 $a(PQKBManifestationID)11382289 035 $a(PQKBTitleCode)TC0000599792 035 $a(PQKBWorkID)10598880 035 $a(PQKB)10315781 035 $a(MiAaPQ)EBC858694 035 $a(CaSebORM)9780123869814 035 $a(Au-PeEL)EBL858694 035 $a(CaPaEBR)ebr10528203 035 $a(CaONFJC)MIL341027 035 $a(PPN)170604071 035 $a(EXLCZ)992550000000084147 100 $a20120124d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProbability and random processes$b[electronic resource] $ewith applications to signal processing and communications /$fScott L. Miller, Donald Childers 205 $aEd. 2. 210 $aWaltham, Mass. $cElsevier$d2012 215 $a1 online resource (625 p.) 300 $aDescription based upon print version of record 311 $a0-12-810245-4 311 $a0-12-386981-1 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Probability and Random Processes: With Applications to Signal Processingand Communications; Copyright; Contents; Preface; Chapter 1: Introduction; 1.1 A Speech Recognition System; 1.2 A Radar System; 1.3 A Communication Network; Chapter 2: Introduction to Probability Theory; 2.1 Experiments, Sample Spaces, and Events; 2.2 Axioms of Probability; 2.3 Assigning Probabilities; 2.4 Joint and Conditional Probabilities; 2.5 Basic Combinatorics; 2.6 Bayes's Theorem; 2.7 Independence; 2.8 Discrete Random Variables; 2.9 Engineering Application-An Optical Communication System; Exercises 327 $aSection 2.1: Experiments, Sample Spaces, and EventsSection 2.2: Axioms of Probability; Section 2.3: Assigning Probabilities; Section 2.4: Joint and Conditional Probabilities; Section 2.5: Basic Combinatorics; Section 2.6: Bayes's Theorem; Section 2.7: Independence; Section 2.8: Discrete Random Variables; Miscellaneous Problems; MATLAB Exercises; Chapter 3: Random Variables, Distributions,and Density Functions; 3.1 The Cumulative Distribution Function; 3.2 The Probability Density Function; 3.3 The Gaussian Random Variable; 3.4 Other Important Random Variables; 3.4.1 Uniform Random Variable 327 $a3.4.2 Exponential Random Variable3.4.3 Laplace Random Variable; 3.4.4 Gamma Random Variable; 3.4.5 Erlang Random Variable; 3.4.6 Chi-Squared Random Variable; 3.4.7 Rayleigh Random Variable; 3.4.8 Rician Random Variable; 3.4.9 Cauchy Random Variable; 3.5 Conditional Distribution and Density Functions; 3.6 Engineering Application: Reliability and Failure Rates; Exercises; Section 3.1: The Cumulative Distribution Function; Section 3.2: The Probability Density Function; Section 3.3: The Gaussian Random Variable; Section 3.4: Other Important Random Variables 327 $aSection 3.5: Conditional Distribution and Density FunctionsSection 3.6: Reliability and Failure Rates; Miscellaneous Exercises; MATLAB Exercises; Chapter 4: Operations on a Single Random Variable; 4.1 Expected Value of a Random Variable; 4.2 Expected Values of Functions of Random Variables; 4.3 Moments; 4.4 Central Moments; 4.5 Conditional Expected Values; 4.6 Transformations of Random Variables; 4.6.1 Monotonically Increasing Functions; 4.6.2 Monotonically Decreasing Functions; 4.6.3 Nonmonotonic Functions; 4.7. Characteristic Functions; 4.8. Probability-Generating Functions 327 $a4.9 Moment-Generating Functions4.10 Evaluating Tail Probabilities; 4.11 Engineering Application-Scalar Quantization; 4.12 Engineering Application-Entropy and Source Coding; Exercises; Section 4.1: Expected Values of a Random Variable; Section 4.2: Expected Values of Functions of a Random Variable; Section 4.3: Moments; Section 4.4: Central Moments; Section 4.5: Conditional Expected Values; Section 4.6: Transformations of Random Variables; Section 4.7: Characteristic Functions; Section 4.8: Probability-Generating Functions; Section 4.9: Moment-Generating Functions 327 $aSection 4.10: Evaluating Tail Probabilities 330 $aMiller and Childers have focused on creating a clear presentation of foundational concepts with specific applications to signal processing and communications, clearly the two areas of most interest to students and instructors in this course. It is aimed at graduate students as well as practicing engineers, and includes unique chapters on narrowband random processes and simulation techniques. The appendices provide a refresher in such areas as linear algebra, set theory, random variables, and more. Probability and Random Processes also includes applications in digital communicat 606 $aSignal processing$xMathematics 606 $aProbabilities 606 $aStochastic processes 615 0$aSignal processing$xMathematics. 615 0$aProbabilities. 615 0$aStochastic processes. 676 $a621.382/20151 700 $aMiller$b Scott L$0223807 701 $aChilders$b Donald G$01549987 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778937503321 996 $aProbability and random processes$93808439 997 $aUNINA