Journal
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS
Volume 10, Issue 5, Pages -Publisher
WILEY
DOI: 10.1002/wics.1435
Keywords
Bayesian analysis; computational statistics; convergence of algorithms; efficiency of algorithms; Hamiltonian Monte Carlo; Monte Carlo methods; Rao-Blackwellisation; simulation; tempering
Categories
Funding
- EPSRC [EP/K014463/1]
- REA grant [PCOFUND-GA-2013-609102]
- Marie Curie Fellowship [FP7/2007-2013]
- Fulbright program
- Agence Nationale de la Recherche of France under PISCES project [ANR-17-CE40-0031-01]
- Chinese Government (CSC)
- Institut Universitaire de France [2016-2021]
- EPSRC [EP/K014463/1] Funding Source: UKRI
Ask authors/readers for more resources
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, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Algorithms and Computational Methods > Algorithms Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available