Distribution of the Maximum of a Gaussian Process by Monte Carlo.

Abstract

First a simple practical procedure for approximating a stationary Gaussian process over a finite interval by a trigonometric polynomial with predetermined error is described. The approximation is then used to calculate the distribution of the maximum, using a novel Monte Carlo method with a control variable which drastically reduces the variance. Finally, the outlined approach is compared to the moving-average technique and shown to be superior for continuous-time, narrow-band processes.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1986
Accession Number
ADA175029

Entities

People

  • A. M. Hasofer

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Cyber
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computers
  • Data Science
  • Distribution Functions
  • Estimators
  • Gaussian Processes
  • Information Science
  • Intervals
  • Monte Carlo Method
  • Normal Distribution
  • North Carolina
  • Polynomials
  • Probability
  • Random Variables
  • Simulations
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.