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References: Stochastic Processes and Random Signals

  1. Stochastic process - Wikipedia - Introduces indexed random variables, sample paths, distributions, stationarity, major process families, and applications across science and engineering.

  2. White noise - Wikipedia - Explains ideal and bandlimited white noise, flat power spectral density, autocorrelation, Gaussian models, and physical interpretations.

  3. Spectral density - Wikipedia - Covers energy and power spectral density, autocorrelation relationships, estimation methods, and interpretation of random signals.

  4. Probability, Random Variables, and Stochastic Processes (4th Edition) - Athanasios Papoulis and S. Unnikrishna Pillai - McGraw-Hill - Papoulis’ transformation-centered examples connect probability models directly to engineering signals and systems.

  5. Random Data: Analysis and Measurement Procedures (4th Edition) - Julius S. Bendat and Allan G. Piersol - Wiley - Bendat and Piersol popularized practical confidence-interval and averaging guidance for correlation and spectral estimates from measured data.

  6. An Introduction to Statistical Signal Processing - Robert M. Gray and Lee D. Davisson - Free textbook develops random variables, processes, stationarity, correlation, spectral density, and linear estimation for engineers.

  7. EE 278 Probability and Statistical Inference Notes - Stanford University - Integrates random processes, stationarity, PSD, white noise, cross spectra, Wiener filtering, and prediction.

  8. Engineering Statistics Handbook - NIST/SEMATECH - Provides engineering-oriented probability distributions, exploratory methods, estimation procedures, examples, and reference tables.

  9. SciPy Spectral Analysis - SciPy - Documents periodograms, Welch estimates, cross-spectral density, coherence, and spectrogram tools for analyzing random data.

  10. Random Processes Lecture Notes - Stanford University - Defines random processes and develops IID, Gaussian, Markov, mean, autocorrelation, and engineering noise examples.