### Abstract

Bayesian analyses of spatial data often use a conditionally autoregressive (CAR) prior, which can be written as the kernel of an improper density that depends on a precision parameter τ that is typically unknown. To include τ in the Bayesian analysis, the kernel must be multiplied by τ^{k} for some k. This article rigorously derives k = (n - I)/2 for the L_{2} norm CAR prior (also called a Gaussian Markov random field model) and k = n - I for the L_{1} norm CAR prior, where n is the number of regions and I the number of "islands" (disconnected groups of regions) in the spatial map. Since I = 1 for a spatial structure defining a connected graph, this supports Knorr-Held's (2002, in Highly Structured Stochastic Systems, 260-264) suggestion that k = (n - 1)/2 in the L_{2} norm case, instead of the more common k = n/2. We illustrate the practical significance of our results using a periodontal example.

Original language | English (US) |
---|---|

Pages (from-to) | 317-322 |

Number of pages | 6 |

Journal | Biometrics |

Volume | 59 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2003 |

### Keywords

- Bayesian analysis
- Gaussian Markov random field model
- Improper prior
- Markov chain Monte Carlo (MCMC) methods
- Periodontal data

## Fingerprint Dive into the research topics of 'On the precision of the conditionally autoregressive prior in spatial models'. Together they form a unique fingerprint.

## Cite this

*Biometrics*,

*59*(2), 317-322. https://doi.org/10.1111/1541-0420.00038