Poisson/gamma random field models for spatial statistics

Published

Journal Article

Doubly stochastic Bayesian hierarchical models are introduced to account for uncertainty and spatial variation in the underlying intensity measure for point process models. Inhomogeneous gamma process random fields and, more generally, Markov random fields with infinitely divisible distributions are used to construct positively autocorrelated intensity measures for spatial Poisson point processes; these in turn are used to model the number and location of individual events. A data augmentation scheme and Markov chain Monte Carlo numerical methods are employed to generate samples from Bayesian posterior and predictive distributions. The methods are developed in both continuous and discrete settings, and are applied to a problem in forest ecology. Bayesian mixture model; Bioabundance; Cox process; Data augmentation; Levy process; Markov chain Monte Carlo; Simulation.

Full Text

Duke Authors

Cited Authors

  • Wolpert, RL; Ickstadt, K

Published Date

  • January 1, 1998

Published In

Volume / Issue

  • 85 / 2

Start / End Page

  • 251 - 267

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/85.2.251

Citation Source

  • Scopus