Random processes

OpenStax

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Probability distributions

Aims and motivation for the course

1 Probability distributions

2 Conditional probabilities and bayes' rule

3 Joint and conditional cdfs and pdfs

Random vectors, signals and functions

1 Random signals

2 Approximation formulae for the gaussian error integral, q(x)

Expectations, moments, and characteristic functions

1 Important examples of expectation

2 Sums of random variables

3 Characteristic functions

Correlation functions and power spectra

Random processes

1 Correlation and covariance

4 Spectral properties of random signals

4.1 Relation of power spectral density to acf

4.2 Linear system (filter) with wss input

4.3 Physical interpretation of power spectral density

5 White and coloured processes

Source: OpenStax, Random processes. OpenStax CNX. Jan 22, 2004 Download for free at http://cnx.org/content/col10204/1.3

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