Results 11 to 20 of about 14,253 (214)
Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation. [PDF]
Stat Med, 2016 The Expected Value of Perfect Partial Information (EVPPI) is a decision‐theoretic measure of the ‘cost’ of parametric uncertainty in decision making used principally in health economic decision making.
Heath A, Manolopoulou I, Baio G.
europepmc +7 more sources
ISPRS International Journal of Geo-Information, 2016 This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA).
Hui Luan, Matthew Quick, Jane Law
doaj +4 more sources
Fitting complex ecological point process models with integrated nested Laplace approximation [PDF]
Methods in Ecology and Evolution, 2013 Summary We highlight an emerging statistical method, integrated nested Laplace approximation ( INLA ), which is ideally suited for fitting complex models to many of the rich spatial data ...
J. Illian +6 more
semanticscholar +6 more sources
Methods in Ecology and Evolution, 2012 Summary1. Spatial analysis of ecological data is central to many interesting questions in ecology. Bayesian implementation of spatially explicit models has received increasing attention from ecologists as Monte Carlo Markov Chain (MCMC) methods have become freely accessible. MCMC simulations offer a flexible framework for modelling extensive ecological
J. Beguin +3 more
semanticscholar +4 more sources
A review of R-packages for random-intercept probit regression in small clusters [PDF]
Frontiers in Applied Mathematics and Statistics, 2016 Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based ...
Haeike Josephy, Tom Loeys, Yves Rosseel
doaj +5 more sources
Computationally efficient Bayesian inference for semi-parametric joint models of competing risks survival and skewed longitudinal data using integrated nested Laplace approximation. [PDF]
BMC Med Res MethodolJoint modeling is widely used in medical research to properly analyze longitudinal biomarkers and survival outcomes simultaneously and to guide appropriate interventions in public health.
Ferede MM, Nakhaei Rad N, Chen DG.
europepmc +2 more sources
Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation. [PDF]
Theor Appl Genet, 2016 Key messageA novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented.Abstract Multi-trait genetic parameter estimation is a relevant topic in ...
Mathew B +4 more
europepmc +2 more sources
Ecological Modelling, 2022 Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale spatial distribution predictions of harbour porpoise ( Phocoena ...
Laura D. Williamson +6 more
semanticscholar +3 more sources
The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models [PDF]
Journal of Computational and Graphical Statistics, 2019 This article introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data.
J. Martínez-Minaya +4 more
semanticscholar +4 more sources
Multivariate posterior inference for spatial models with the integrated nested Laplace approximation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2018 The integrated nested Laplace approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov random field.
V. Gómez‐Rubio, F. Palmí-Perales
semanticscholar +3 more sources