Results 181 to 190 of about 21,991 (210)

Spatial variability and uncertainty associated with soil moisture content using INLA-SPDE combined with PyMC3 probability programming

Scientific Reports
Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model.
Yujian Yang, Xueqin Tong
semanticscholar   +1 more source

Anomalous Dispersion of LO Phonons inLa.1.85Sr0.15CuO4

Journal of Low Temperature Physics, 1999
The dispersion of the highest energy LO phonon branch in La. 1.85 Sr 0.15 CuO 4 in the (100) direction has been reinvestigated by high resolution inelastic neutron scattering. In contrast to what has been recently reported by McQueeney et al. (Phys.
Lothar Pintschovius, Markus Braden
openaire   +1 more source

Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems

Conference on Uncertainty in Artificial Intelligence
Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular techniques such as ensemble methods that handle high-dimensional, nonlinear PDE priors focus mostly on state estimation ...
Rafael Anderka, M. Deisenroth, So Takao
semanticscholar   +1 more source

A data fusion model for meteorological data using the INLA-SPDE method

Journal of the Royal Statistical Society Series C: Applied Statistics
We present a data fusion model designed to address the problem of sparse observational data by incorporating numerical forecast models as an additional data source to improve predictions of key variables.
S. J. Villejo, Sara Martino, F. Lindgren, Janine Illian   +3 more
semanticscholar   +1 more source

Spatial and Spatio-temporal Bayesian Models with R - INLA

, 2015
M. Blangiardo, M. Cameletti
semanticscholar   +3 more sources

Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping

Science of The Total Environment, 2017
Understanding the uncertainty in spatial modelling of environmental variables is important because it provides the end-users with the reliability of the maps. Over the past decades, Bayesian statistics has been successfully used. However, the conventional simulation-based Markov Chain Monte Carlo (MCMC) approaches are often computationally intensive ...
Jingyi Huang, Brendan P. Malone, Budiman Minasny, Alex B. McBratney, John Triantafilis   +4 more
openaire   +2 more sources

Case studies in Bayesian computation using INLA

2010
Latent Gaussian models are a common construct in statistical applications where a latent Gaussian field, indirectly observed through data, is used to model, for instance, time and space dependence or the smooth effect of covariates. Many well-known statistical models, such as smoothing-spline models, space time models, semiparametric regression ...
Sara Martino, Håvard Rue
openaire   +1 more source

A Review of INLA

2022
Nalini Ravishanker, Balaji Raman, Refik Soyer   +2 more
openaire   +1 more source

Numerical Recipes for Landslide Spatial Prediction Using R-INLA

2019
The geomorphological community typically assesses the landslide susceptibility at the catchment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates.
Lombardo, Luigi, Opitz, Thomas, Huser, Raphaël   +2 more
openaire   +3 more sources

Bayesian Hierarchical Models for the Combination of Spatially Misaligned Data: A Comparison of Melding and Downscaler Approaches Using INLA and SPDE

Journal of Agricultural Biological and Environmental Statistics, 2023
Ruiman Zhong, Paula Moraga
semanticscholar   +1 more source