A sparse matrix approach to Bayesian computation in large linear models
From MaRDI portal
Publication:956783
DOI10.1016/S0167-9473(02)00252-9zbMath1429.62302OpenAlexW2084833933WikidataQ59859664 ScholiaQ59859664MaRDI QIDQ956783
Darren J. Wilkinson, Stephen Kh Yeung
Publication date: 26 November 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00252-9
linear systemssparse matricesMarkov chain Monte Carlo (MCMC)precision matrixmultivariate normalblock samplinglocal computationGaussian modelsBayes linear models
Related Items (2)
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach ⋮ 2nd special issue on matrix computations and statistics
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Fast Sampling of Gaussian Markov Random Fields
- Space-varying regression models: specifications and simulation
- Calculating posterior distributions and modal estimates in Markov mixture models
- Geometric ergodicity of Gibbs and block Gibbs samplers for a hierarchical random effects model
- Markov chains for exploring posterior distributions. (With discussion)
- Marginal Likelihood from the Gibbs Output
- The Calculation of Posterior Distributions by Data Augmentation
- Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models
- Probabilistic Networks and Expert Systems
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- On Block Updating in Markov Random Field Models for Disease Mapping
- Detecting Homogeneous Segments in DNA Sequences by Using Hidden Markov Models
- Efficient parametrisations for normal linear mixed models
- Parallel algorithms for linear models. Numerical methods and estimation problems
This page was built for publication: A sparse matrix approach to Bayesian computation in large linear models
