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 …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

Gaussian processes and random fields have a long history, covering multiple approaches to
representing spatial and spatio-temporal dependence structures, such as covariance …

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 …

Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties,
and the environment in many areas. Investigators have for long attempted to estimate …

Spatial processes are central to many ecological processes, but fitting models that
incorporate spatial correlation to data from ecological surveys is computationally …

Spatial data is crucial to improve decision-making in a wide range of fields including
environment, health, ecology, urban planning, economy, and society. Spatial Statistics for …

Species distribution models are popular and widely applied ecological tools. Recent
increases in data availability have led to opportunities and challenges for species …