Bayesian neural networks for nonlinear time series forecasting
Statistics and Computing
Journal of Computational Physics
Digital Signal Processing
Bayesian inference with optimal maps
Journal of Computational Physics
Panel discussion: integrating data from multiple simulation models of different fidelity
Proceedings of the Winter Simulation Conference
Journal of Computational Physics
Mathematics and Computers in Simulation
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The Bayesian approach allows one to easily quantify uncertainty, at least in theory. In practice, however, the Markov chain Monte Carlo (MCMC) method can be computationally expensive, particularly in complicated inverse problems. We present a methodology for improving the speed and efficiency of an MCMC analysis by combining runs on different scales. By using a coarser scale, the chain can run faster (particularly when there is an external forward simulator involved in the likelihood evaluation) and better explore the posterior, being less likely to become stuck in local maxima. We discuss methods for linking the coarse chain back to the original fine-scale chain of interest. The resulting coupled chain can thus be run more efficiently without sacrificing the accuracy achieved at the finer scale