{"product_id":"probability-and-stochastic-processes-a-friendly-introduction-for-electrical-and-computer-engineers-international-adaptation-paperback-softback-9781394304226","title":"Probability and Stochastic Processes; A Friendly Introduction for Electrical and Computer Engineers, International Adaptation (Paperback \/ softback) 9781394304226","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eProbability and Stochastic Processes\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Friendly Introduction for Electrical and Computer Engineers, International Adaptation\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRoy D. Yates (Author), David J. Goodman (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394304226, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 16 December 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e576 pages\u003cbr\u003e23.4 x 18.8 x 3.2 cm, 0.888 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eProbability and Stochastic Processes – A Friendly Introduction for Electrical and Computer Engineers, Fourth Edition \u003c\/i\u003eserves as an accessible guide for engineering students delving into the realms of probability theory and stochastic processes.This text strikes a balance between rigorous mathematical exposition and clear, intuitive explanations, ensuring that students grasp the fundamental concepts essential for applying mathematics to real-world engineering challenges. Enhanced with the practical MATLAB applications. The book offers students valuable hands-on experienceto reinforce the theoretical material.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eThis International adaptation has been thoroughly revised and updated. Notably, it includes a new chapter on Probabilistic Inequalities and Bounds. The sections on Stochastic Processes and Sums of Random Variables have been comprehensively enhanced to encompass additional topics, aligning with the latest curriculum requirements. With an array of new and updated examples, quizzes, and end-of-chapter problems, the book provides robust support to students, particularly in bridging the gap between theoretical probability and its practical applications in engineering.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface vii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Random Experiments, Models, and Probabilities 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started with Probability 1\u003c\/p\u003e \u003cp\u003e1.1 Applying Set Theory to Probability 2\u003c\/p\u003e \u003cp\u003e1.2 Probability Axioms 7\u003c\/p\u003e \u003cp\u003e1.3 Conditional Probability 10\u003c\/p\u003e \u003cp\u003e1.4 Partitions and the Law of Total Probability 13\u003c\/p\u003e \u003cp\u003e1.5 Bayes’ Theorem 17\u003c\/p\u003e \u003cp\u003e1.6 Independence 18\u003c\/p\u003e \u003cp\u003e1.7 Matlab 22\u003c\/p\u003e \u003cp\u003eProblems 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Sequential Random Experiments 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Tree Diagrams 31\u003c\/p\u003e \u003cp\u003e2.2 Counting Methods 35\u003c\/p\u003e \u003cp\u003e2.3 Independent Trials 43\u003c\/p\u003e \u003cp\u003e2.4 Matlab 46\u003c\/p\u003e \u003cp\u003eProblems 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Discrete Random Variables 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Definitions 53\u003c\/p\u003e \u003cp\u003e3.2 Probability Mass Function 56\u003c\/p\u003e \u003cp\u003e3.3 Families of Discrete Random Variables 59\u003c\/p\u003e \u003cp\u003e3.4 Cumulative Distribution Function (CDF) 65\u003c\/p\u003e \u003cp\u003e3.5 Averages and Expected Value 69\u003c\/p\u003e \u003cp\u003e3.6 Functions of a Random Variable 74\u003c\/p\u003e \u003cp\u003e3.7 Expected Value of a Derived Random Variable 77\u003c\/p\u003e \u003cp\u003e3.8 Variance and Standard Deviation 80\u003c\/p\u003e \u003cp\u003e3.9 Matlab 86\u003c\/p\u003e \u003cp\u003eProblems 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Continuous Random Variables 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Continuous Sample Space 103\u003c\/p\u003e \u003cp\u003e4.2 The Cumulative Distribution Function 105\u003c\/p\u003e \u003cp\u003e4.3 Probability Density Function 108\u003c\/p\u003e \u003cp\u003e4.4 Expected Values 113\u003c\/p\u003e \u003cp\u003e4.5 Families of Continuous Random Variables 116\u003c\/p\u003e \u003cp\u003e4.6 Gaussian Random Variables 122\u003c\/p\u003e \u003cp\u003e4.7 Delta Functions, Mixed Random Variables 128\u003c\/p\u003e \u003cp\u003e4.8 Matlab 134\u003c\/p\u003e \u003cp\u003eProblems 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multiple Random Variables 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Joint Cumulative Distribution Function 146\u003c\/p\u003e \u003cp\u003e5.2 Joint Probability Mass Function 149\u003c\/p\u003e \u003cp\u003e5.3 Marginal PMF 152\u003c\/p\u003e \u003cp\u003e5.4 Joint Probability Density Function 154\u003c\/p\u003e \u003cp\u003e5.5 Marginal PDF 159\u003c\/p\u003e \u003cp\u003e5.6 Independent Random Variables 161\u003c\/p\u003e \u003cp\u003e5.7 Expected Value of a Function of Two Random Variables 164\u003c\/p\u003e \u003cp\u003e5.8 Covariance, Correlation and Independence 167\u003c\/p\u003e \u003cp\u003e5.9 Bivariate Gaussian Random Variables 174\u003c\/p\u003e \u003cp\u003e5.10 Multivariate Probability Models 178\u003c\/p\u003e \u003cp\u003e5.11 Matlab 183\u003c\/p\u003e \u003cp\u003eProblems 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Probability Models of Derived Random Variables 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 PMF of a Function of Two Discrete Random Variables 200\u003c\/p\u003e \u003cp\u003e6.2 Functions Yielding Continuous Random Variables 201\u003c\/p\u003e \u003cp\u003e6.3 Functions Yielding Discrete or Mixed Random Variables 207\u003c\/p\u003e \u003cp\u003e6.4 Continuous Functions of Two Continuous Random Variables 211\u003c\/p\u003e \u003cp\u003e6.5 PDF of the Sum of Two Random Variables 214\u003c\/p\u003e \u003cp\u003e6.6 Matlab 216\u003c\/p\u003e \u003cp\u003eProblems 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Conditional Probability Models 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Conditioning a Random Variable by an Event 225\u003c\/p\u003e \u003cp\u003e7.2 Conditional Expected Value Given an Event 231\u003c\/p\u003e \u003cp\u003e7.3 Conditioning Two Random Variables by an Event 233\u003c\/p\u003e \u003cp\u003e7.4 Conditioning by a Random Variable 237\u003c\/p\u003e \u003cp\u003e7.5 Conditional Expected Value Given a Random Variable 241\u003c\/p\u003e \u003cp\u003e7.6 Bivariate Gaussian Random Variables: Conditional PDFs 245\u003c\/p\u003e \u003cp\u003e7.7 Matlab 248\u003c\/p\u003e \u003cp\u003eProblems 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Random Vectors 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Vector Notation 257\u003c\/p\u003e \u003cp\u003e8.2 Independent Random Variables and Random Vectors 260\u003c\/p\u003e \u003cp\u003e8.3 Functions of Random Vectors 261\u003c\/p\u003e \u003cp\u003e8.4 Expected Value Vector and Correlation Matrix 265\u003c\/p\u003e \u003cp\u003e8.5 Gaussian Random Vectors 270\u003c\/p\u003e \u003cp\u003e8.6 Matlab 277\u003c\/p\u003e \u003cp\u003eProblems 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Sums of Random Variables 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Expected Values of Sums 285\u003c\/p\u003e \u003cp\u003e9.2 Moment Generating Functions 289\u003c\/p\u003e \u003cp\u003e9.3 MGF of the Sum of Independent Random Variables 293\u003c\/p\u003e \u003cp\u003e9.4 Characteristic Function and Probability Generating Function 297\u003c\/p\u003e \u003cp\u003e9.5 Matlab 301\u003c\/p\u003e \u003cp\u003eProblems 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Hypothesis Testing 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Significance Testing 308\u003c\/p\u003e \u003cp\u003e10.2 Binary Hypothesis Testing 311\u003c\/p\u003e \u003cp\u003e10.3 Multiple Hypothesis Test 324\u003c\/p\u003e \u003cp\u003e10.4 Matlab 327\u003c\/p\u003e \u003cp\u003eProblems 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Estimation of a Random Variable 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Minimum Mean Square Error Estimation 339\u003c\/p\u003e \u003cp\u003e11.2 Linear Estimation of X given Y 344\u003c\/p\u003e \u003cp\u003e11.3 MAP and ML Estimation 349\u003c\/p\u003e \u003cp\u003e11.4 Linear Estimation of Random Variables from Random Vectors 353\u003c\/p\u003e \u003cp\u003e11.5 Matlab 360\u003c\/p\u003e \u003cp\u003eProblems 362\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Some Probabilistic Inequalities and Bounds 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Markov Inequality 369\u003c\/p\u003e \u003cp\u003e12.2 Chebyshev’s Inequality 373\u003c\/p\u003e \u003cp\u003e12.3 Chernoff Bound 374\u003c\/p\u003e \u003cp\u003e12.4 Central Limit Theorem 376\u003c\/p\u003e \u003cp\u003e12.5 Sample Mean and Variance 380\u003c\/p\u003e \u003cp\u003e12.6 Laws of Large Numbers (LLN) 382\u003c\/p\u003e \u003cp\u003eProblems 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Stochastic Processes and Markov Chains 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Definitions and Examples 391\u003c\/p\u003e \u003cp\u003e13.2 Random Variables from Random Processes 397\u003c\/p\u003e \u003cp\u003e13.3 Independent, Identically Distributed Random Sequences 399\u003c\/p\u003e \u003cp\u003e13.4 The Poisson Process 400\u003c\/p\u003e \u003cp\u003e13.5 Properties of the Poisson Process 404\u003c\/p\u003e \u003cp\u003e13.6 The Brownian Motion Process 407\u003c\/p\u003e \u003cp\u003e13.7 Markov Process 409\u003c\/p\u003e \u003cp\u003e13.8 Discrete-Time Markov Chains 410\u003c\/p\u003e \u003cp\u003e13.9 Higher Transition Probabilities: Chapman–Kolmogorov Equations 414\u003c\/p\u003e \u003cp\u003e13.10 Long-Run Behavior of Markov Chains 419\u003c\/p\u003e \u003cp\u003e13.11 Classification of States of Chains 422\u003c\/p\u003e \u003cp\u003e13.12 Markov Chains with Countably Infinite States 426\u003c\/p\u003e \u003cp\u003e13.13 Ergodic and Reducible Chains 429\u003c\/p\u003e \u003cp\u003e13.14 Birth Process and Death Process 433\u003c\/p\u003e \u003cp\u003e13.15 Queuing Models – Poisson Queues 435\u003c\/p\u003e \u003cp\u003e13.16 Matlab 441\u003c\/p\u003e \u003cp\u003eProblems 447\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Stationary Processes and Random Signal Processing 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Expected Value and Correlation 457\u003c\/p\u003e \u003cp\u003e14.2 Stationary Processes 460\u003c\/p\u003e \u003cp\u003e14.3 Wide Sense Stationary Processes 463\u003c\/p\u003e \u003cp\u003e14.4 Cross-Correlation 466\u003c\/p\u003e \u003cp\u003e14.5 Gaussian Processes 469\u003c\/p\u003e \u003cp\u003e14.6 Linear Filtering of Continuous-Time Stochastic Processes 471\u003c\/p\u003e \u003cp\u003e14.7 Linear Filtering of a Random Sequence 475\u003c\/p\u003e \u003cp\u003e14.8 Discrete-Time Linear Filtering: Vectors and Matrices 481\u003c\/p\u003e \u003cp\u003e14.9 Power Spectral Density of a Continuous-Time Process 485\u003c\/p\u003e \u003cp\u003e14.10 Power Spectral Density of a Random Sequence 490\u003c\/p\u003e \u003cp\u003e14.11 Cross Power Spectral Density 494\u003c\/p\u003e \u003cp\u003e14.12 Frequency Domain Filter Relationships 496\u003c\/p\u003e \u003cp\u003e14.13 Matlab 501\u003c\/p\u003e \u003cp\u003eProblems 510\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A The Sample Mean 517\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA. 1 Sample Mean: Expected Value and Variance 517\u003c\/p\u003e \u003cp\u003eA. 2 Deviation of a Random Variable from the Expected Value 519\u003c\/p\u003e \u003cp\u003eA. 3 Laws of Large Numbers 523\u003c\/p\u003e \u003cp\u003eA. 4 Point Estimates of Model Parameters 525\u003c\/p\u003e \u003cp\u003eA. 5 Confidence Intervals 531\u003c\/p\u003e \u003cp\u003eA. 6 Matlab 538\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Families of Random Variables 541\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB. 1 Discrete Random Variables 541\u003c\/p\u003e \u003cp\u003eB. 2 Continuous Random Variables 543\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C A Few Math Facts 547\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 553\u003c\/p\u003e \u003cp\u003eIndex 555\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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