A Network Representation of the Multiprocess Dynamic Linear Model,

Abstract

Dempster 1 has characterized the dynamic linear model (DLM) as a probabilistic belief network, showing that recent algorithms for propagation of information in such networks generalize Kalman filtering, prediction and smoothing algorithms for the DLM. Recently the Bayesian network technology has been extended to model mixed discrete and continuous random variables using conditional Gaussian (CG) distributions 5 with analogous propagation schemes 6. This paper applies the theory of CG probability networks to characterize the multiprocess dynamic linear model (MPDLM) and its requisite computations in a unified way. The complexity of exact computations is determined and approximate methods are proposed.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007127

Entities

People

  • David Tritchler
  • Sharon-lise Normand

Organizations

  • Harvard Medical School

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computations
  • Computer Science
  • Data Science
  • Information Science
  • Kalman Filtering
  • Models
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Theoretical Computer Science

Fields of Study

  • Computer science

Readers

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

Technology Areas

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