Contents
PART 1 RANDOM SIGNALS BACKGROUND
1 Probability and Random Variables: A Review
1.2 Intuitive Notion of Probability
1.5 Joint and Conditional Probability, Bayes Rule and Independence
1.6 Continuous Random Variables and Probability Density Function
1.7 Expectation, Averages, and Characteristic Function
1.8 Normal or Gaussian Random Variables
1.9 Impulsive Probability Density Functions
1.10 Joint Continuous Random Variables
1.11 Correlation, Covariance, and Orthogonality
1.12 Sum of Independent Random Variables and Tendency Toward Normal Distribution
1.13 Transformation of Random Variables
1.14 Multivariate Normal Density Function
1.15 Linear Transformation and General Properties of Normal Random Variables
1.16 Limits, Convergence, and Unbiased Estimators
1.17 A Note on Statistical Estimators
2 Mathematical Description of Random Signals
2.1 Concept of a Random Process
2.2 Probabilistic Description of a Random Process
2.4 Stationarity, Ergodicity, and Classification of Processes
2.7 Power Spectral Density Function
2.10 Narrowband Gaussian Process
2.11 Wiener or Brownian-Motion Process
2.13 Determination of Autocorrelation and Spectral Density Functions from Experimental Data
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