We propose a Bayesian model selection approach for generalized linear mixed models
(GLMMs). We consider covariance structures for the random effects that are widely used in …

Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional
Autoregressive Priors Page 1 Bayesian Analysis (2024) 19, Number 4, pp. 985–1011 …

We propose a novel class of dynamic factor models for spatiotemporal areal data. This novel
class of models assumes that the spatiotemporal process may be represented by some few …

Fast algorithms are developed for Bayesian analysis of Gaussian hierarchical models with
intrinsic conditional autoregressive (ICAR) spatial random effects. To achieve computational …

Chapter 1 discusses the use of proper Gaussian Markov random fields (GMRFs) as building
blocks for highly structured models for Gaussian areal data. In this context, the use of proper …

We propose a novel class of multiscale spatiotemporal models for multivariate Gaussian
data. First, we decompose the multivariate data and the underlying latent process with a …

Chapter 2 summarizes recent developments related to intrinsic conditional autoregressive
(ICAR) models for Gaussian areal data that make inference for these models fast, automatic …

Objective Bayesian Analysis for Gaussian Hierarchical Models with Intrinsic Conditional
Autoregressive Priors Page 1 Bayesian Analysis (2019) 14, Number 1, pp. 181–209 Objective …

Entering the era of knowledge economy, the economic value of talent education is more and
more emphasized, and it contributes more and more to the local economy and injects new …

Latent Space (LS) network models project the nodes of a network on a $ d $-dimensional
latent space to achieve dimensionality reduction of the network while preserving its relevant …