The following pages link to INLA (Q19562):
Displaying 40 items.
- Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes (Q104464) (← links)
- Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) (Q257643) (← links)
- Direct fitting of dynamic models using integrated nested Laplace approximations -- INLA (Q434960) (← links)
- Grid based variational approximations (Q452525) (← links)
- Bayesian inference for additive mixed quantile regression models (Q452530) (← links)
- A Bayesian approach to fitting Gibbs processes with temporal random effects (Q484712) (← links)
- Improving the INLA approach for approximate Bayesian inference for latent Gaussian models (Q902211) (← links)
- Bayesian multiscale feature detection of log-spectral densities (Q961849) (← links)
- Bayesian computing with INLA: new features (Q1615084) (← links)
- Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range (Q1621330) (← links)
- Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (Q1622175) (← links)
- sppmix: Poisson point process modeling using normal mixture models (Q1729311) (← links)
- Variational message passing for elaborate response regression models (Q1738138) (← links)
- Section on the special year for Mathematics of Planet Earth (MPE 2013) (Q1939988) (← links)
- Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures (Q2028571) (← links)
- Spatiotemporal probabilistic wind vector forecasting over Saudi Arabia (Q2044259) (← links)
- Where is the clean air? A Bayesian decision framework for personalised cyclist route selection using R-INLA (Q2057372) (← links)
- Erlang mixture modeling for Poisson process intensities (Q2066740) (← links)
- Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods (Q2080350) (← links)
- Non-homogeneous Poisson process intensity modeling and estimation using measure transport (Q2108510) (← links)
- A case study competition among methods for analyzing large spatial data (Q2272997) (← links)
- Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates (Q2291306) (← links)
- A primer on disease mapping and ecological regression using \({\mathtt{INLA}}\) (Q2513344) (← links)
- Spatiotemporal wildfire modeling through point processes with moderate and extreme marks (Q2686054) (← links)
- Approximate Bayesian Estimation for Multivariate Count Time Series Models (Q2806333) (← links)
- Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany (Q2857479) (← links)
- Competing risks joint models using R-INLA (Q3389291) (← links)
- Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months (Q3389297) (← links)
- (Q3463718) (← links)
- (Q5023000) (← links)
- Spatial modelling of risk premiums for water damage insurance (Q5073017) (← links)
- Fitting logistic multilevel models with crossed random effects via Bayesian Integrated Nested Laplace Approximations: a simulation study (Q5106968) (← links)
- Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information (Q5130552) (← links)
- Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Q5147863) (← links)
- Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes (Q5222497) (← links)
- Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA (Q5230004) (← links)
- Spatial and Spatio‐temporal Bayesian Models with R‐INLA (Q5251248) (← links)
- Bringing Bayesian Models to Life (Q5382496) (← links)
- Bayesian Modeling of Spatio-Temporal Data with R (Q5862534) (← links)
- Comments on: ``Comparing and selecting spatial predictors using local criteria'' (Q5971082) (← links)
