Sara Martino
Cited by
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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
H Rue, S Martino, N Chopin
Journal of the Royal Statistical Society Series B: Statistical Methodologyá…, 2009
Approximate Bayesian inference for hierarchical Gaussian Markov random field models
H Rue, S Martino
Journal of statistical planning and inference 137 (10), 3177-3192, 2007
Implementing approximate Bayesian inference using Integrated Nested Laplace Approximation: A manual for the inla program
S Martino, H Rue
Department of Mathematical Sciences, NTNU, Norway, 2009
Approximate Bayesian inference for survival models
S Martino, R Akerkar, H Rue
Scandinavian Journal of Statistics 38 (3), 514-528, 2011
INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion
H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski
R package version 0.0-1404466487, URL http://www. R-INLA. org, 2014
Fitting complex ecological point process models with integrated nested Laplace approximation
JB Illian, S Martino, SH S°rbye, JB Gallego‐Fernßndez, M Zunzunegui, ...
Methods in Ecology and Evolution 4 (4), 305-315, 2013
INLA: functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation
H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski
R package version 0.0, 2009
Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation
J Beguin, S Martino, H Rue, SG Cumming
Methods in Ecology and Evolution 3 (5), 921-929, 2012
Animal models and integrated nested Laplace approximations
AM Holand, I Steinsland, S Martino, H Jensen
G3: Genes, Genomes, Genetics 3 (8), 1241-1251, 2013
Integrated nested Laplace approximations (INLA)
S Martino, A Riebler
arXiv preprint arXiv:1907.01248, 2019
Approximate Bayesian inference in spatial generalized linear mixed models
J Eidsvik, S Martino, H Rue
Scandinavian journal of statistics 36 (1), 1-22, 2009
Case studies in Bayesian computation using INLA
S Martino, H Rue
Complex data modeling and computationally intensive statistical methods, 99-114, 2010
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field
R Ingebrigtsen, F Lindgren, I Steinsland, S Martino
Spatial Statistics 14, 338-364, 2015
GMRFLib: a C-library for fast and exact simulation of Gaussian Markov random fields
H Rue, T Follestad
SIS-2002-236, 2001
Estimating stochastic volatility models using integrated nested laplace approximations
S Martino, K Aas, O Lindqvist, LR Neef, H Rue
The European Journal of Finance 17 (7), 487-503, 2011
Effects of increased wind power generation on mid-Norway’s energy balance under climate change: a market based approach
B Franšois, S Martino, LS T°fte, B Hingray, B Mo, JD Creutin
Energies 10 (2), 227, 2017
Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach
G Fioravanti, S Martino, M Cameletti, G Cattani
Atmospheric Environment 248, 118192, 2021
Integration of presence‐only data from several sources: a case study on dolphins' spatial distribution
S Martino, DS Pace, S Moro, E Casoli, D Ventura, A Frachea, M Silvestri, ...
Ecography 44 (10), 1533-1543, 2021
Implementing approximate Bayesian inference for survival analysis using integrated nested Laplace approximations
R Akerkar, S Martino, H Rue
Prepr Stat Nor Univ Sci Technol 1, 1-38, 2010
Spatial modelling of temperature and humidity using systems of stochastic partial differential equations
X Hu, I Steinsland, D Simpson, S Martino, H Rue
arXiv preprint arXiv:1307.1402, 2013
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