Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

With the expansion in the quantity and types of biodiversity data being collected, there is a
need to find ways to combine these different sources to provide cohesive summaries of …

Many spatially resolved transcriptomic technologies do not have single-cell resolution but
measure the average gene expression for each spot from a mixture of cells of potentially …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells …

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 …

Replicability takes on special meaning when researching phenomena that are embedded in
space and time, including phenomena distributed on the surface and near surface of the …

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.
It explains how to analyze functional data, both at exploratory and inferential levels. It also …

The mountain cryosphere of mainland Europe is recognized to have important impacts on a
range of environmental processes. In this paper, we provide an overview on the current …

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