Information-Theoretic Non-Parametric Unimodal Density Estimation.

Abstract

This document presents an information-theoretic method for nonparametric density estimation which guarantees that the resulting density is unimodal. The method inputs data in the form of moment or quantile information and consequently can handle both data derived and non-data derived information. In the non-data derived situation it yields a method for obtaining unimodal Bayesian prior distribution. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1984
Accession Number
ADA144522

Entities

People

  • Abraham Charnes
  • K. Paick
  • P. Brockett

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Ambient Noise
  • Background Noise
  • Classification
  • Computations
  • Convex Programming
  • Data Science
  • Detection
  • Engineering
  • Factor Analysis
  • Information Science
  • Information Theory
  • Probability
  • Random Variables
  • Security
  • Signal Detection
  • Signal Processing
  • Statistics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference