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 …

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 …

Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable
species such as sharks, is a major challenge for contemporary fisheries management. Here …

Abstract The Integrated Nested Laplace Approximation (INLA) has established itself as a
widely used method for approximate inference on Bayesian hierarchical models which can …

This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and
demonstrates the geostatsp and dieasemapping packages for performing inference using …

Disease maps are effective tools for explaining and predicting patterns of disease outcomes
across geographical space, identifying areas of potentially elevated risk, and formulating …

Background: Studies on the extent to which long-term exposure to ambient particulate matter
(PM) with aerodynamic diameter≤ 2.5 μ m (PM 2.5) contributes to adult mortality in India are …

Health and development indicators (HDIs) such as vaccination coverage are regularly
measured in many low‐and middle‐income countries using household surveys, often due to …

This article presents a methodology for modeling aggregated disease incidence data with
the spatially continuous log-Gaussian Cox process. Statistical models for spatially …