Results 31 to 40 of about 518,510 (312)
Causal inference with spatio‐temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq [PDF]
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020 Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio‐temporal point processes.
Georgia Papadogeorgou +3 more
semanticscholar +1 more source
Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields [PDF]
Journal of the American Statistical Association, 2021 A multivariate distribution can be described by a triangular transport map from the target distribution to a simple reference distribution. We propose Bayesian nonparametric inference on the transport map by modeling its components using Gaussian ...
M. Katzfuss, Florian Schäfer
semanticscholar +1 more source
PLoS ONE, 2012 Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics.
Andrea Bertolo +5 more
openaire +4 more sources
REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification [PDF]
Geoscientific Model DevelopmentSurface heat flow is a geophysical variable that is affected by a complex combination of various heat generation and transport processes. The processes act on different lengths scales, from tens of meters to hundreds of kilometers.
M. J. Ziebarth +2 more
doaj +1 more source
Remote Sensing, 2021 The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars.
Yu Tao +3 more
doaj +1 more source
Modeling spatial tail dependence with Cauchy convolution processes
, 2022 We study the class of dependence models for spatial data obtained from Cauchy convolution processes based on different types of kernel functions. We show that the resulting spatial processes have appealing tail dependence properties, such as tail ...
Huser, Raphaël, Krupskiy, Pavel
core +1 more source
The impact of experimental design choices on parameter inference for models of growing cell colonies [PDF]
Royal Society Open Science, 2018 To better understand development, repair and disease progression, it is useful to quantify the behaviour of proliferative and motile cell populations as they grow and expand to fill their local environment.
Andrew Parker +2 more
doaj +1 more source
, 2017 . Spatial point pattern data describes locations of events observed over a given domain, with the number of and locations of these events being random.
Thomas J. Leininger, A. Gelfand
semanticscholar +1 more source
Bayesian inference for high-dimensional nonstationary Gaussian processes
Journal of Statistical Computation and Simulation, 2019 In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a ...
M. Risser, Daniel Turek
semanticscholar +1 more source
Annals of GISThe inference of causation is essential for interpretable scientific research and evidence-based policymaking. Existing causal methods – including those based on intervention, forecasting, and graph models – have achieved considerable success in non ...
Bingrong Chen +7 more
doaj +1 more source