PROBABILITY DENSITY FUNCTION ESTIMATION (WITH APPLICATIONS TO RECEIVER DESIGN FOR RECEPTION IN NON-GAUSSIAN NOISE)

Abstract

This note considers the problem of estimation of an unknown probability density function, p(x), and the derivative of the logarithm of that density function, f(x), given N samples from an ensemble whose probability density is p(x). Our principle objective is to estimate f(x) since it has been shown that the optimal receiver for known threshold signals in additive white (but possibly non-Gaussian) noise consists of a filter matched to the signal preceded by a no-memory device. Although our approach is primarily motivated toward obtaining a good estimate of f(x), the method and results would appear also to be applicable to finding 'good' estimates of p(x). The 'goodness' of the estimation procedure is investigated theoretically and experimentally.

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Document Details

Document Type
Technical Report
Publication Date
Aug 29, 1969
Accession Number
AD0694552

Entities

People

  • James E Evans

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Computational Science
  • Data Science
  • Distribution Functions
  • Equations
  • Gaussian Noise
  • Information Science
  • Maximum Likelihood Estimation
  • Memory Devices
  • Pattern Recognition
  • Power Series
  • Probabilistic Models
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.