Accession Number : ADP007127
Title : A Network Representation of the Multiprocess Dynamic Linear Model,
Corporate Author : HARVARD MEDICAL SCHOOL BOSTON MA
Personal Author(s) : Normand, Sharon-Lise ; Tritchler, David
Report Date : 1992
Pagination or Media Count : 4
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.
Descriptors : *KALMAN FILTERING, *PROBABILITY, *RANDOM VARIABLES, ALGORITHMS, COMPUTATIONS, DYNAMICS, FILTRATION, MODELS, NETWORKS, PAPER, PHOSGENE, PREDICTIONS, PROPAGATION, THEORY, VARIABLES.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE