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Combining regional climate model output via a multivariate Markov random field model

Journal Article


Abstract


  • Conditional autoregressive (CAR) models, and the more general Markov random field models, are

    excellent tools for analyzing data laid out on spatial lattices. These lattices may be regular, such

    as the grids associated with images, remote-sensing data, climate models, etc., or irregular, such as

    U.S. census divisions (counties, tracts, or block-groups) or other administrative units. Besag (1974)

    laid out the basic framework for Markov random field models. For random variables y1,...,yn observed

    at n locations on the lattice structure, the collection of conditional distributions f(yi|y−i), i = 1,...,n

    (where y−i refers to all random variables except the ith one) can be combined under certain regularity

    conditions to form a joint distribution f(y1,...,yn). Rue and Held (2006) can be consulted for an

    excellent exposition of the theory of Markov random fields; see also the reviews in the texts by Cressie

    (1993), Banerjee et al. (2004), and Schabenberger and Gotway (2005). CAR models are Gaussian

    Markov random fields. It should be noted that they are not the same as the so-called simultaneously

    specified autoregressive (SAR) models and are actually a more general construc

Authors


  •   Sain, Stephan R. (external author)
  •   Furrer, Reinhard (external author)
  •   Cressie, Noel A.

Publication Date


  • 2007

Citation


  • Sain, S., Furrer, R. & Cressie, N. (2007). Combining regional climate model output via a multivariate Markov random field model. Bulletin of the International Statistical Institute, 62 1375-1382. Lisbon, Portugal Proceedings of the 56th Session of the International Statistical Institute

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5975

Number Of Pages


  • 7

Start Page


  • 1375

End Page


  • 1382

Volume


  • 62

Place Of Publication


  • https://www.isi-web.org/index.php/publications/proceedings

Abstract


  • Conditional autoregressive (CAR) models, and the more general Markov random field models, are

    excellent tools for analyzing data laid out on spatial lattices. These lattices may be regular, such

    as the grids associated with images, remote-sensing data, climate models, etc., or irregular, such as

    U.S. census divisions (counties, tracts, or block-groups) or other administrative units. Besag (1974)

    laid out the basic framework for Markov random field models. For random variables y1,...,yn observed

    at n locations on the lattice structure, the collection of conditional distributions f(yi|y−i), i = 1,...,n

    (where y−i refers to all random variables except the ith one) can be combined under certain regularity

    conditions to form a joint distribution f(y1,...,yn). Rue and Held (2006) can be consulted for an

    excellent exposition of the theory of Markov random fields; see also the reviews in the texts by Cressie

    (1993), Banerjee et al. (2004), and Schabenberger and Gotway (2005). CAR models are Gaussian

    Markov random fields. It should be noted that they are not the same as the so-called simultaneously

    specified autoregressive (SAR) models and are actually a more general construc

Authors


  •   Sain, Stephan R. (external author)
  •   Furrer, Reinhard (external author)
  •   Cressie, Noel A.

Publication Date


  • 2007

Citation


  • Sain, S., Furrer, R. & Cressie, N. (2007). Combining regional climate model output via a multivariate Markov random field model. Bulletin of the International Statistical Institute, 62 1375-1382. Lisbon, Portugal Proceedings of the 56th Session of the International Statistical Institute

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5975

Number Of Pages


  • 7

Start Page


  • 1375

End Page


  • 1382

Volume


  • 62

Place Of Publication


  • https://www.isi-web.org/index.php/publications/proceedings