On MCMC sampling in hierarchical longitudinal models
Statistics and Computing
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
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
Bayesian inference for α-stable distributions: A random walk MCMC approach
Computational Statistics & Data Analysis
Parallel exact sampling and evaluation of Gaussian Markov random fields
Computational Statistics & Data Analysis
Parallelizing MCMC for Bayesian spatiotemporal geostatistical models
Statistics and Computing
Block sampler and posterior mode estimation for asymmetric stochastic volatility models
Computational Statistics & Data Analysis
Estimating stochastic volatility models using daily returns and realized volatility simultaneously
Computational Statistics & Data Analysis
Block Kalman Filtering for Large-Scale DSGE Models
Computational Economics
Efficient Bayesian estimation of multivariate state space models
Computational Statistics & Data Analysis
Bayesian analysis of the stochastic conditional duration model
Computational Statistics & Data Analysis
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
GPU accelerated MCMC for modeling terrorist activity
Computational Statistics & Data Analysis
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Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. Improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use available information to make better predictions of the future states of the chain and increase the efficiency of prefetching considerably. The optimal acceptance rate for the prefetching random walk Metropolis-Hastings algorithm is obtained for a special case and it is shown to decrease in the number of processors employed. The performance of the algorithms is illustrated using a well-known macroeconomic model. Bayesian estimation of DSGE models, linearly or nonlinearly approximated, is identified as a potential area of application for prefetching methods. The generality of the proposed method, however, suggests that it could be applied in other contexts as well.