The key operation in Bayesian inference is to compute high-dimensional integrals. An old
approximate technique is the Laplace method or approximation, which dates back to Pierre …

Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Geospatial health data are essential to inform public health and policy. These data can be
used to quantify disease burden, understand geographic and temporal patterns, identify risk …

In recent years, disease map** studies have become a routine application within
geographical epidemiology and are typically analysed within a Bayesian hierarchical model …

During the last three decades, Bayesian methods have developed greatly in the field of
epidemiology. Their main challenge focusses around computation, but the advent of Markov …

The INLA approach for approximate Bayesian inference for latent Gaussian models has
been shown to give fast and accurate estimates of posterior marginals and also to be a …

Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …

The integrated nested Laplace approximation (INLA) provides an interesting way of
approximating the posterior marginals of a wide range of Bayesian hierarchical models. This …

The projection of age‐stratified cancer incidence and mortality rates is of great interest due
to demographic changes, but also therapeutical and diagnostic developments. Bayesian …

Objectives To measure access to opioid treatment programs (OTPs) and office-based
buprenorphine treatment (OBBTs) at the smallest geographic unit for which the Census …