Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

The objective of multi-object estimation is to simultaneously estimate the number of objects
and their states from a set of observations in the presence of data association uncertainty …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Spatial point processes are mathematical models used to describe and analyse the
geometrical structure of patterns formed by objects that are irregularly or randomly …

Functional data analysis (FDA), which is a branch of statistics on modeling infinite
dimensional random vectors resided in functional spaces, has become a major research …

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

Individual-based forest ecology and management, where the focus is on individual plants in
the context of their populations, draws on a large body of different quantitative methods that …

The problem of jointly detecting multiple objects and estimating their states from image
observations is formulated in a Bayesian framework by modeling the collection of states as a …

Spatial analysis methods have seen a rapid rise in popularity due to demand from a wide
range of fields. These include, among others, biology, spatial economics, image processing …