Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
Explore this paper's citation graph
Summary
This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.
- Type
- article
- Published
- 2009-04-01
- Cited by
- 5,314
- References
- 237
- OpenAlex
- https://openalex.org/W2144898279
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1657669
Keywords
Laplace's method, Markov chain Monte Carlo, Mathematics, Bayesian inference, Computer science
References
- Parameter Orthogonality and Approximate Conditional Inference
- Disease Mapping of Stage‐Specific Cancer Incidence Data
- Estimation and model identification for continuous spatial processes
- Interpolation of spatial data
- Variational approximations for logistic mixed models
- Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression
- Constrained Monte Carlo Maximum Likelihood for Dependent Data
- Gaussian Markov Random Fields: Theory and Applications
- Model-based geostatistics - Discussion
- PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE
- Bayesian measures of model complexity and fit
- The theory of statistics
- Explicit construction of GMRF approximations to generalised Matérn fields on irregular grids
- Discussion on the paper by Rue, Martino and Chopin: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
- Hierarchical Generalized Linear Models
- Sequential Monte Carlo Methods in Practice
- Sampling from the posterior distribution in generalized linear mixed models
- Bayesian parameter estimation via variational methods
- Studies in Trend Detection of Scatter Plots with Visualization
- Iterative methods for sparse linear systems
Cited by
- Bayes Factors via MCMC and Path Sampling - The case of Factor Models
- Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
- Restricted Covariance Priors with Applications in Spatial Statistics
- Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference
- Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.
- Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors
- Risk factors for rotavirus infection in pigs in Busia and Teso subcounties, Western Kenya
- Deploying digital health data to optimize influenza surveillance at national and local scales
- Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors
- Quantifying multiple pressure interactions affecting populations of a recreationally and commercially important freshwater fish
- Latent Gaussian Models for Topic Modeling
- Crack Nucleation and Branching in the eXtended Finite Element Method
- Factors influencing the distribution of brown trout (Salmo trutta) in a mountain stream: Implications for brown trout invasion success
- Snow integrated communicable disease prediction service
- Using Storm for scaleable sequential statistical inference
- Rehabilitation of improper correlation matrices
- Modelling spatiotemporal patterns of childhood HIV/TB related mortality and malnutrition: applications to Agincourt data in rural South Africa
- Clustering and Registration of Functional Data with Applications in Time Course Genomics Data
- Industrial Location and Space: New Insights
- MCMC for Generalized Linear Mixed Models with glmmBUGS
Related papers
- Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
- Adaptive Metropolis-coupled MCMC for BEAST 2
- Bayesian MCMC Approach to Learning About the SIR Model
- River water quality modelling and simulation based on Markov Chain Monte Carlo computation and Bayesian inference model
- Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters
- Bayesian seismic inversion: Measuring Langevin MCMC sample quality with kernels
- Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods