Bayes linear variance adjustment for time series

1996/04/02 by Darren J. Wilkinson, Darren J Wilkinson, Wilkinson, Darren J
Computer Science · Engineering · Physics and Astronomy · #Bayesian Modeling and Causal Inference #Data Analysis #FOS: Physical sciences #Fault Detection and Control Systems #Neural Networks and Applications #Statistics and Probability (physics.data-an) #bayes-an #physics.data-an

paper · pdf · doi:10.48550/arxiv.bayes-an/9604001

LaTeX2e, 13 pages including 7 figures. Also available from http://fourier.dur.ac.uk:8000/~dma1djw/pub/djwll.html

arxiv created 1996/04/02 · openalex publication_date 1996/04/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper exhibits quadratic products of linear combinations of observables which identify the covariance structure underlying the univariate locally linear time series dynamic linear model. The first- and second-order moments for the joint distribution over these observables are given, allowing Bayes linear learning for the underlying covariance structure for the time series model. An example is given which illustrates the methodology and highlights the practical implications of the theory.

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