Special issue on parallel processing and statistics
Computational Statistics & Data Analysis - Special issue on parallel processing and statistics
Space-varying regression models: specifications and simulation
Computational Statistics & Data Analysis - Special issue: Computational econometrics
Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models
Statistics and Computing
Handbook of Parallel Computing and Statistics (Statistics, Textbooks and Monographs)
Handbook of Parallel Computing and Statistics (Statistics, Textbooks and Monographs)
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Computational Statistics & Data Analysis
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
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Markov chain Monte Carlo algorithms are computationally expensive for large models. Especially, the so-called one-block Metropolis-Hastings (M-H) algorithm demands large computational resources, and parallel computing seems appealing. A parallel one-block M-H algorithm for latent Gaussian Markov random field (GMRF) models is introduced. Important parts of this algorithm are parallel exact sampling and evaluation of GMRFs. Parallelisation is achieved with parallel algorithms from linear algebra for sparse symmetric positive definite matrices. The parallel GMRF sampler is tested for GMRFs on lattices and irregular graphs, and gives both good speed-up and good scalability. The parallel one-block M-H algorithm is used to make inference for a geostatistical GMRF model with a latent spatial field of 31,500 variables.