A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations
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Publication:830465
DOI10.1016/j.csda.2020.107147OpenAlexW3109964134WikidataQ114191901 ScholiaQ114191901MaRDI QIDQ830465
Stephen Joe, Paul T. Brown, Håvard Rue, Chaitanya K. Joshi
Publication date: 7 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.09880
low discrepancy sequencesBayesian inferenceintegrated nested Laplace approximationsmarginal approximations
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- Grid based variational approximations
- Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
- Statistical decision theory. Estimation, testing, and selection.
- Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
- The existence of good extensible rank-1 lattices
- Bayesian computing with INLA: new features
- Monte Carlo and quasi-Monte Carlo sampling
- High dimensional integration of smooth functions over cubes
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Variance Reduction via Lattice Rules
- Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
- Statistical Inference for Spatial Processes
- On the Second‐Order Random Walk Model for Irregular Locations
- Accurate Approximations for Posterior Moments and Marginal Densities
- On the Runge Example
- On multimodality of the likelihood in the spatial linear model
- Very large fractional factorial and central composite designs
- Gaussian Markov Random Fields
- Spatial and Spatio‐temporal Bayesian Models with R‐INLA
- Introduction to Quasi-Monte Carlo Integration and Applications
- On Information and Sufficiency
- Checking for prior-data conflict
