scholarly journals Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook

2021 ◽  
Vol 4 (4) ◽  
pp. 2000237 ◽  
Author(s):  
Peter G. Bolhuis ◽  
David W. H. Swenson
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Path Sampling ◽  
Transition Path ◽  
Future Outlook ◽  
Transition Path Sampling ◽  
Software Applications

scholarly journals Ensemble Metropolis Light Transport

2022 ◽  
Vol 41 (1) ◽  
pp. 1-15
Author(s):  
Thomas Bashford-Rogers ◽  
Ls Paulo Santos ◽  
Demetris Marnerides ◽  
Kurt Debattista
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Light Transport ◽  
Path Sampling ◽  
Informed Decisions ◽  
Space Saving ◽  
Local Path

This article proposes a Markov Chain Monte Carlo ( MCMC ) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

On Markov Chain Monte Carlo Acceleration

1994 ◽  
Author(s):  
Alan E. Gelfand ◽  
Sujit K. Sahu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo
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