2013/05/22 by Luke Bornn, Bornn, Luke
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.1305.5017
Proceedings of BAYSM, 2013
arxiv created 2013/05/22 · arxiv updated 2013/05/23
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and other stochastic approximation methods have frequently been applied within the simulated tempering framework, this note demonstrates through a simple example the additional improvements brought about by parallelization, adaptive proposals and automated bin splitting.