The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Railway maintenance planners require a predictive model that can assess the railway track
geometry degradation. The present paper uses a Hierarchical Bayesian model as a tool to …

Abstract Objectives Explore Bayesian spatio-temporal methods to analyse local patterns of
crime change over time at the small-area level through an application to property crime data …

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from
the basics of disease mapping to the most advanced topics in this field. A multidimensional …

This paper explores hierarchical Bayesian models that can be used to predict rail track
geometry degradation and thus guide planning maintenance and renewal actions …

The conditional autoregressive model and the intrinsic autoregressive model are widely
used as prior distribution for random spatial effects in Bayesian models. Several authors …

In Bayesian disease mapping, one needs to specify a neighborhood structure to make
inference about the underlying geographical relative risks. We propose a model in which the …

Birth history data—the primary source of data on under-5 mortality in developing countries—
are infrequently used for subnational estimates due to concerns over small sample sizes. In …

Joint analysis of longitudinal and survival data has received increasing attention in the
recent years, especially for analyzing cancer and AIDS data. As both repeated …