Estimation and Reconstruction for Stochastic Processes and Deterministic Functions.

Abstract

Method for statistical estimation of parameters of partially observed stochastic processes and for minimum mean squared error reconstruction of unobserved portions of sample paths (state estimation) were developed. Some of these methods apply to models of random distributions of particles in space or events in time; others apply to Markov processes. Statistical estimators are asymptotically exact even though certain of the unknown parameters are infinite-dimensional. For several classes of processes the problem of simultaneously performing parameter estimation and state estimation was solved. Refined techniques for reconstructing a deterministic signal from hard-limited data were devised. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 14, 1983
Accession Number
ADA136571

Entities

People

  • A. F. Karr

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Estimators
  • Information Science
  • Markov Processes
  • Mathematics
  • Multivariate Analysis
  • Observation
  • Operations Research
  • Probability
  • Random Variables
  • Sampling
  • Scientific Research
  • Signal Processing
  • Statistical Inference
  • Stochastic Processes
  • Universities

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space