Probability, Random Variables, and Random Processes Theory and Signal Processing Applications.

Detalles Bibliográficos
Autor principal: Shynk, John J.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • PROBABILITY, RANDOM VARIABLES, AND RANDOM PROCESSES
  • CONTENTS
  • PREFACE
  • NOTATION
  • 1 Overview and Background
  • 1.1 Introduction
  • 1.1.1 Signals, Signal Processing, and Communications
  • 1.1.2 Probability, Random Variables, and Random Vectors
  • 1.1.3 Random Sequences and Random Processes
  • 1.1.4 Delta Functions
  • 1.2 Deterministic Signals and Systems
  • 1.2.1 Continuous Time
  • 1.2.2 Discrete Time
  • 1.2.3 Discrete-Time Filters
  • 1.2.4 State-Space Realizations
  • 1.3 Statistical Signal Processing with MATLAB®
  • 1.3.1 Random Number Generation
  • 1.3.2 Filtering
  • Problems
  • Further Reading
  • PART I Probability, Random Variables, and Expectation
  • 2 Probability Theory
  • 2.1 Introduction
  • 2.2 Sets and Sample Spaces
  • 2.3 Set Operations
  • 2.4 Events and Fields
  • 2.5 Summary of a Random Experiment
  • 2.6 Measure Theory
  • 2.7 Axioms of Probability
  • 2.8 Basic Probability Results
  • 2.9 Conditional Probability
  • 2.10 Independence
  • 2.11 Bayes' Formula
  • 2.12 Total Probability
  • 2.13 Discrete Sample Spaces
  • 2.14 Continuous Sample Spaces
  • 2.15 Nonmeasurable Subsets of R
  • Problems
  • Further Reading
  • 3 Random Variables
  • 3.1 Introduction
  • 3.2 Functions and Mappings
  • 3.3 Distribution Function
  • 3.4 Probability Mass Function
  • 3.5 Probability Density Function
  • 3.6 Mixed Distributions
  • 3.7 Parametric Models for Random Variables
  • 3.8 Continuous Random Variables
  • 3.8.1 Gaussian Random Variable (Normal)
  • 3.8.2 Log-Normal Random Variable
  • 3.8.3 Inverse Gaussian Random Variable (Wald)
  • 3.8.4 Exponential Random Variable (One-Sided)
  • 3.8.5 Laplace Random Variable (Double-Sided Exponential)
  • 3.8.6 Cauchy Random Variable
  • 3.8.7 Continuous Uniform Random Variable
  • 3.8.8 Triangular Random Variable
  • 3.9.1 Bernoulli Random Variable
  • 3.9.2 Binomial Random Variable
  • 3.9.3 Geometric Random Variable (with Support Z+ or N)
  • 3.9.4 Negative Binomial Random Variable (Pascal)
  • 3.9.5 Poisson Random Variable
  • 3.9.6 Hypergeometric Random Variable
  • 3.9.7 Discrete Uniform Random Variable
  • 3.9.8 Logarithmic Random Variable (Log-Series)
  • 3.9.9 Zeta Random Variable (Zipf)
  • Problems
  • Further Reading
  • 4 Multiple Random Variables
  • 4.1 Introduction
  • 4.2 Random Variable Approximations
  • 4.2.1 Binomial Approximation of Hypergeometric
  • 4.2.2 Poisson Approximation of Binomial

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