Multiscale Smoothing Error Models

Abstract

A class of multiscale stochastic models based on scale-recursive dynamics on trees has recently been introduced. These models are interesting because they can be used to represent a broad class of physical phenomena and because they lead to efficient algorithms for estimation and likelihood calculation. In this paper, we provide a complete statistical characterization of the error associated with smoothed estimates of the multiscale stochastic processes described by these models. In particular, we show that the smoothing error is itself a multiscale stochastic process with parameters which can be explicitly calculated.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1994
Accession Number
ADA459332

Entities

People

  • Alan S. Willsky
  • Mark R. Luettgen

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Covariance
  • Equations
  • Image Processing
  • Information Operations
  • Markov Models
  • Markov Processes
  • Measurement
  • Models
  • Multiscale Models
  • Power Spectra
  • Signal Processing
  • Standards
  • Statistics
  • Stochastic Processes
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.