Bayes Nets of Time Series: Stochastic Realizations and Projections

P. E. Caines (), R. Deardon () and H. P. Wynn ()
Additional contact information
P. E. Caines: McGill University
R. Deardon: University of Guelph
H. P. Wynn: University of Guelph

Chapter 7 in Optimal Design and Related Areas in Optimization and Statistics, 2009, pp 155-166 from Springer

Abstract: Summary Graphical models in which every node holds a time-series are developed using special conditions from static multivariate Gaussian processes, particularly the notion of lattice conditional independence (LCI), due to Anderson and Perlman (1993). Under certain “feedback free” conditions, LCI imposes a special zero structure on the state space representation of processes which have a stochastic realisation. This structure comes directly from the transitive directed acyclic graph (TDAG) which is in one-to-one correspondence with the Boolean Hilbert lattice of the LCO formulation. Simple AR(1) examples are presented.

Keywords: Conditional Independence; Multivariate Normal Distribution; Gaussian Case; Boolean Lattice; Conditional Covariance (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-79936-0_7

Ordering information: This item can be ordered from
http://www.springer.com/9780387799360

DOI: 10.1007/978-0-387-79936-0_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().