Results 11 to 20 of about 3,927,001 (165)

Bayesian Integrated Nested Laplace Approximation (INLA) Longevity Bonds Market Model

open access: yesRisks
Pricing coupon longevity bonds (CLBs) is challenging in illiquid markets due to the incompleteness of insurance markets and the unavailability of longevity payout data.
Yethu Sithole, Samuel Asante Gyamerah
doaj   +3 more sources

Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia

open access: yesBMC Public Health
Introduction Dengue is a mosquito-borne disease caused by the dengue virus, primarily transmitted by Aedes aegypti and Aedes albopictus. Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction
Marko Ferdian Salim   +2 more
doaj   +3 more sources

Spatial Data Analysis with R-INLA with Some Extensions [PDF]

open access: yesJournal of Statistical Software, 2015
The integrated nested Laplace approximation (INLA) provides an interesting way of approximating the posterior marginals of a wide range of Bayesian hierarchical models.
Roger Bivand   +2 more
doaj   +2 more sources

Bayesian Spatial Modelling with R-INLA [PDF]

open access: yesJournal of Statistical Software, 2015
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally ...
Finn Lindgren, Håvard Rue
doaj   +4 more sources

A Fast, Flexible Simulation Framework for Bayesian Adaptive Designs-The R Package BATSS. [PDF]

open access: yesPharm Stat
ABSTRACT The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software package (Bayesian Adaptive Trials Simulator Software—BATSS) for the statistical software R, which provides a flexible structure for the fast simulation of Bayesian ...
Couturier DL   +4 more
europepmc   +2 more sources

Spatial analysis of road crash frequency using Bayesian models with Integrated Nested Laplace Approximation (INLA) [PDF]

open access: yesJournal of Transportation Safety & Security, 2020
Improving traffic safety is a priority of most transportation agencies around the world. As part of traffic safety management strategies, efforts have focused on developing more accurate crash-freq...
Romi Satria   +2 more
openaire   +2 more sources

Geographical Pattern of Testicular Cancer Points to Maternal Exposures Behind Increasing Incidence. [PDF]

open access: yesInt J Cancer
ABSTRACT Evidence suggests that testicular cancer (TC) originates from germ cell neoplasia in situ arising during fetal development, implying that prenatal environmental exposures may influence disease risk. We investigated whether maternal place of residence during pregnancy is associated with regional variation in TC incidence.
Kaipia A   +8 more
europepmc   +2 more sources

Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China

open access: yesMathematics, 2023
Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of ...
Zongyuan Xia   +4 more
doaj   +1 more source

Importance Sampling with the Integrated Nested Laplace Approximation [PDF]

open access: yes, 2021
The integrated nested Laplace approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation of posterior marginals for the parameters in the models.
Gómez-Rubio, Virgilio   +4 more
core   +1 more source

Using INLA to fit a complex point process model with temporally varying effects – a case study [PDF]

open access: yes, 2012
Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses
Soerbye, S   +3 more
core   +2 more sources

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