Specifying a Gaussian Markov random field by a sparse cholesky triangle

Hanne Wist, Håvard Rue*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This note discusses the approach of specifying a Gaussian Markov random field (GMRF) by the Cholesky triangle of the precision matrix. A such representation can be made extremely sparse using numerical techniques for incomplete sparse Cholesky factorization, and provide very computational efficient representation for simulating from the GMRF. However, we provide theoretical and empirical justification showing that the sparse Cholesky triangle representation is fragile when conditioning a GMRF on a subset of the variables or observed data, meaning that the computational cost increases.

Original languageEnglish (US)
Pages (from-to)161-176
Number of pages16
JournalCommunications in Statistics: Simulation and Computation
Issue number1
StatePublished - Jan 1 2006
Externally publishedYes


  • Gaussian Markov random field
  • Incomplete Cholesky factorization
  • Parameterization
  • Sparse matrices

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation


Dive into the research topics of 'Specifying a Gaussian Markov random field by a sparse cholesky triangle'. Together they form a unique fingerprint.

Cite this