Results 51 to 60 of about 13,859 (206)

A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia [PDF]

, 2013
This paper outlines a methodology for semi-parametric spatio-temporal modelling of data which is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data
Box G. E. P., Cleveland R. B., Cleveland W. S., Hastie T. J., Health Effects Institute, Matheron G., Rue H.   +6 more
core   +2 more sources

Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics

, 2012
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)
Palacios, JA, Minin, VN
openaire   +3 more sources

Palm distributions for log Gaussian Cox processes [PDF]

, 2015
This paper establishes a remarkable result regarding Palmdistributions for a log Gaussian Cox process: the reduced Palmdistribution for a log Gaussian Cox process is itself a log Gaussian Coxprocess which only differs from the original log Gaussian Cox ...
Coeurjolly, Jean-François, Møller, Jesper, Waagepetersen, Rasmus   +2 more
core   +4 more sources

Spatial data fusion adjusting for preferential sampling using integrated nested Laplace approximation and stochastic partial differential equation

Journal of the Royal Statistical Society: Series A (Statistics in Society)
Spatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field.
Ruiman Zhong, André Victor Ribeiro Amaral, Paula Moraga   +2 more
semanticscholar   +1 more source

Integrated nested Laplace approximations for threshold stochastic volatility models [PDF]

Econometrics and Statistics
Abstract The aim is to implement the integrated nested Laplace approximations (INLA), known to be very fast and efficient, for estimating the parameters of the threshold stochastic volatility (TSV) model. INLA replaces Markov chain Monte Carlo (MCMC) simulations with accurate deterministic approximations. Weakly informative proper priors are used, as
P. de Zea Bermudez, J. Miguel Marín, Håvard Rue, Helena Veiga   +3 more
openaire   +2 more sources

Investigation of the Hourly and Spatial Patterns of Traffic Offenses During March-April 2019 in Iran Using Bivariate Generalized Additive Models and Integrated Nested Laplace Approximation

International journal of high risk behaviors and addiction, 2022
Background: The control, management, and prevention of driving accidents and risky driving are regarded as concerns for numerous countries, according to the World Health Organization.
Mohammad Fayaz, Alireza Abadi, A. Razzaghi, S. Khodakarim, M. Hosseini   +4 more
semanticscholar   +1 more source

Using Integrated Nested Laplace Approximations for Modelling Spatial Healthcare Utilization

, 2012
In recent years, spatial and spatio-temporal modeling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping). In this work we propose different spatial models to study hospital recruitment, including some potentially explicative variables.
MUSIO, MONICA, Sauleau E. A, MAMELI, VALENTINA   +2 more
openaire   +4 more sources

Traffic prediction at signalised intersections using Integrated Nested Laplace Approximation

, 2021
A Bayesian approach to predicting traffic flows at signalised intersections is considered using the the INLA framework. INLA is a deterministic, computationally efficient alternative to MCMC for estimating a posterior distribution. It is designed for latent Gaussian models where the parameters follow a joint Gaussian distribution.
Townsend, D., Nel, C.
openaire   +2 more sources

Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations [PDF]

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2009
Summary Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox
Rue, Havard, Martino, Sara, Chopin, Nicolas   +2 more
openaire   +2 more sources

Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping [PDF]

, 2011
A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to data on general smooth manifolds, and includes both the Gaussian Mat\'{e}rn ...
Bolin, David, Lindgren, Finn
core   +4 more sources