Abstract
In this short note, we show how the parallel adaptive Wang–Landau (PAWL) algorithm of Bornn et al. (J Comput Graph Stat, to appear) 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atchade Y, Liu J (2010) The Wang-Landau algorithm for Monte Carlo computation in general state spaces. Stat Sin 20:209–233
Bornn L, Shaddick G, Zidek J (2012) Modeling nonstationary processes through dimension expansion. J Am Stat Assoc 107(497):281–289
Bornn L, Jacob PE, Del Moral P, Doucet A (2013) An adaptive interacting Wang-Landau algorithm for automatic density exploration. J Comput Graph Stat 22(3):749–773
Geyer C, Thompson E (1995) Annealing Markov chain Monte Carlo with applications to ancestral inference. J Am Stat Assoc 90(431):909–920
Marinari E, Parisi G (1992) Simulated tempering: a new Monte Carlo scheme. Europhys Lett 19(6):451
Wang F, Landau DP (2001) Efficient, multiple-range random walk algorithm to calculate the density of states. Phys Rev Lett 86(10):2050–2053
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bornn, L. (2014). PAWL-Forced Simulated Tempering. In: Lanzarone, E., Ieva, F. (eds) The Contribution of Young Researchers to Bayesian Statistics. Springer Proceedings in Mathematics & Statistics, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-319-02084-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-02084-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02083-9
Online ISBN: 978-3-319-02084-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)