Information, Consistent Estimation and Dynamic System Identification.

Abstract

The asymptotic behavior of parameter estimates and the identification and modeling of dynamical systems are investigated. Measures of the relevant information in a given sequence of observations are defined and shown to possess useful properties, such as the metric property on the parameter set. The convergence of maximum likelihood and related Bayesian estimates for general observation sequences is investigated. The situation where the true parameter is not a member of a given parameter set is considered as well as the situation where the parameter set includes the true model. The finite parameter set case is emphasized for simplicity in the convergence analysis, but the results are extended in general terms to the infinite parameter case.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1976
Accession Number
ADA037972

Entities

People

  • Yoram Baram

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Electrical Engineering
  • Engineering
  • Equations
  • Ergodic Processes
  • Information Science
  • Information Theory
  • Mathematical Models
  • Order Statistics
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Stationary Processes
  • Steady State
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms