Probability, Random Variables, and Stochastic Processes

Papoulis, A., & Pillai, S. U. (2002). Probability, Random Variables, and Stochastic Processes. McGraw-Hill.

Type

Book

Intellectual base

Textbook

Year

2002

Link

https://openlibrary.org/works/OL16931083Wmatched

Cited from

statistics_experimental_design

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Measurement Uncertainty and Observational Noise
  • A noisy thermometer measures an undisturbed (or less disturbed) system imprecisely; a disturbing measurement changes the system, whether the observation is precise or noisy, a separation between observation noise and system perturbation that Papoulis and Pillai (2002) develop within their canonical treatment of probability and stochastic processes.

    This sourceClassic engineering reference on stochastic processes: distinguishes additive observation noise (which corrupts measurement without altering the underlying process) from input perturbation (which drives the process itself).

Domain-specific

• Bates Distribution
  • Affine transformation gives the mean of iid Uniform((a,b)) variables on ([a,b]).
• Cross-Covariance Matrix
  • In stochastic processes, lag-indexed cross-covariance functions generalize the matrix by adding time arguments.

Verification

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