Final published version, 3.62 MB, PDF document
Available under license: CC BY
Final published version
Licence: CC BY
Research output: Contribution to Journal/Magazine › Special issue › peer-review
Bayesian inference and data augmentation schemes for spatial, spatiotemporal and multivariate Log-Gaussian Cox processes in R. / Taylor, Benjamin; Davies, Tilman; Rowlingson, Barry et al.
In: Journal of Statistical Software, Vol. 63, 7, 10.02.2015, p. 1-48.Research output: Contribution to Journal/Magazine › Special issue › peer-review
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TY - JOUR
T1 - Bayesian inference and data augmentation schemes for spatial, spatiotemporal and multivariate Log-Gaussian Cox processes in R
AU - Taylor, Benjamin
AU - Davies, Tilman
AU - Rowlingson, Barry
AU - Diggle, Peter
PY - 2015/2/10
Y1 - 2015/2/10
N2 - Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advances in MCMC methods for these models and their implementation in the R package lgcp. Our suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field. We also present methods for Bayesian inference in two further classes of model based on the log-Gaussian Cox process. The first of these concerns the case where we wish to fit a point process model to data consisting of event-counts aggregated to a set of spatial regions: we demonstrate how this can be achieved using data-augmentation. The second concerns Bayesian inference for a class of marked-point processes specified via a multivariate log-Gaussian Cox process model. For both of these extensions, we give details of their implementation in R.
AB - Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advances in MCMC methods for these models and their implementation in the R package lgcp. Our suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field. We also present methods for Bayesian inference in two further classes of model based on the log-Gaussian Cox process. The first of these concerns the case where we wish to fit a point process model to data consisting of event-counts aggregated to a set of spatial regions: we demonstrate how this can be achieved using data-augmentation. The second concerns Bayesian inference for a class of marked-point processes specified via a multivariate log-Gaussian Cox process model. For both of these extensions, we give details of their implementation in R.
KW - Cox process
KW - R
KW - spatiotemporal point process
KW - multivariate spatial process
KW - Bayesian Inference
KW - MCMC
U2 - 10.18637/jss.v063.i07
DO - 10.18637/jss.v063.i07
M3 - Special issue
VL - 63
SP - 1
EP - 48
JO - Journal of Statistical Software
JF - Journal of Statistical Software
SN - 1548-7660
M1 - 7
ER -