Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target...
Our algorithm improves on the $O(n)$ complexity of regular MCMC by...doi:10.3934/fods.2019005Marco BanterleClara GrazianAnthony LeeChristian P. RobertFoundations of Data ScienceBanterle, M., Grazian, C., Lee, A., Robert, C. P., 2015. Accelerating Metropolis-Hastings algorithms by delayed ...
MCMC methods for functions: modifying old algorithms to make them faster. Statist. Sci. 28, 424–446 (2012). Hinze, M., Pinnau, R., Ulbrich, M. & Ulbrich, S. Optimization with PDE Constraints. Mathematical Modelling: Theory and Applications (Springer, 2008). Kaltenbach, S., Perdikaris,...
Sherlock, C., Thiery, A. and Golightly, A.: Efficiency of delayed-acceptance random walk Metropolis algorithms. arXiv preprintarXiv:1506.08155(2015) Solonen, A., Ollinaho, P., Laine, M., Haario, H., Tamminen, J., Järvinen, H., et al.: Efficient MCMC for climate model parameter e...
type="main" xml:lang="en">\nMarkov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a...
Computer science Accelerating Markov chain Monte Carlo via parallel predictive prefetching HARVARD UNIVERSITY Margo SeltzerRyan P. Adams AngelinoElaine LeeWe present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates ...
Patrick R Conrad, Youssef M Marzouk, Natesh S Pillai, and Aaron Smith, Asymptotically exact MCMC algorithms via local approximations of computationally intensive models, Journal of the American Statistical Association, in press (2015). arXiv:1402.1694....
We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation that uses speculation to predict and complete computational work ahead of ...
We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation that uses speculation to predict and complete computational work ahead of ...
These spatial models are richly parameterized and lend themselves to the structured Markov chain Monte Carlo (SMCMC) algorithms. SMCMC provides a simple, general, and flexible framework for accelerating convergence in an MCMC sampler by providing a systematic way to block groups of similar parameters...