Ensemble preconditioning for Markov chain Monte Carlo simulation
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable.
Code (1)
Similar Papers 제목 키워드 기반
Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion
Large, sparse linear systems are pervasive in modern science and engineering, and Krylov subspace solvers are an established means of solving them. Yet convergence can be slow for ill-conditioned matrices, so practical d…
Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promising recent proposal, microcanonical Langevin Monte Carlo, has shown sta…
Bayesian InferenceAugmented Ensemble MCMC sampling in Factorial Hidden Markov Models
Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape…
Bayesian InferenceTATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications
With the advent of GPU-assisted hardware and maturing high-efficiency software platforms such as TensorFlow and PyTorch, Bayesian posterior sampling for neural networks becomes plausible. In this article we discuss Bayes…
BIG-bench Machine LearningGPUPreconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images -- Extended Version
Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue…
Segmentation