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Probability and stochastic processes : a friendly introduction for electrical and computer engineers /
"In Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, readers are able to grasp the concepts of probability and stochastic processes, and apply these in professional engineering practice. The 3rd edition also includes quiz solutions within the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
[2014]
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| Edición: | Third edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Features of this Text; Who will benefit from using this text?; What's New?; Notable Features; Instructor Support; Preface; Welcome to the third edition; How the book is organized; What is distinctive about this book?; Further Reading; Acknowledgments; A Message to Students from the Authors; Contents; Chapter 1: Experiments, Models, and Probabilities; Getting Started with Probability; 1.1 Set Theory; 1.2 Applying Set Theory to Probability; 1.3 Probability Axioms; 1.4 Conditional Probability; 1.5 Partitions and the Law of Total Probability; 1.6 Independence.
- 1.7 MatlabProblems; Chapter 2: Sequential Experiments; 2.1 Tree Diagrams; 2.2 Counting Methods; 2.3 Independent Trials; 2.4 Reliability Analysis; 2.5 Matlab; Problems; Chapter 3: Discrete Random Variables; 3.1 Definitions; 3.2 Probability Mass Function; 3.3 Families of Discrete Random Variables; 3.4 Cumulative Distribution Function (CDF); 3.5 Averages and Expected Value; 3.6 Functions of a Random Variable; 3.7 Expected Value of a Derived Random Variable; 3.8 Variance and Standard Deviation; 3.9 Matlab; Problems; Chapter 4: Continuous Random Variables; 4.1 Continuous Sample Space.
- 4.2 The Cumulative Distribution Function4.3 Probability Density Function; 4.4 Expected Values; 4.5 Families of Continuous Random Variables; 4.6 Gaussian Random Variables; 4.7 Delta Functions, Mixed Random Variables; 4.8 Matlab; Problems; Chapter 5: Multiple Random Variables; 5.1 Joint Cumulative Distribution Function; 5.2 Joint Probability Mass Function; 5.3 Marginal PMF; 5.4 Joint Probability Density Function; 5.5 Marginal PDF; 5.6 Independent Random Variables; 5.7 Expected Value of a Function of Two Random Variables; 5.8 Covariance, Correlation and Independence.
- 5.9 Bivariate Gaussian Random Variables5.10 Multivariate Probability Models; 5.11 Matlab; Problems; Chapter 6: Probability Models of Derived Random Variables; 6.1 PMF of a Function of Two Discrete Random Variables; 6.2 Functions Yielding Continuous Random Variables; 6.3 Functions Yielding Discrete or Mixed Random Variables; 6.4 Continuous Functions of Two Continuous Random Variables; 6.5 PDF of the Sum of Two Random Variables; 6.6 Matlab; Problems; Chapter 7: Conditional Probability Models; 7.1 Conditioning a Random Variable by an Event; 7.2 Conditional Expected Value Given an Event.
- 7.3 Conditioning Two Random Variables by an Event7.4 Conditioning by a Random Variable; 7.5 Conditional Expected Value Given a Random Variable; 7.6 Bivariate Gaussian Random Variables: Conditional PDFs; 7.7 Matlab; Problems; Chapter 8: Random Vectors; 8.1 Vector Notation; 8.2 Independent Random Variables and Random Vectors; 8.3 Functions of Random Vectors; 8.4 Expected Value Vector and Correlation Matrix; 8.5 Gaussian Random Vectors; 8.6 Matlab; Problems; Chapter 9: Sums of Random Variables; 9.1 Expected Values of Sums; 9.2 Moment Generating Functions.