scholarly journals Using Noise to Speed up Markov Chain Monte Carlo Estimation

2015 ◽  
Vol 53 ◽  
pp. 113-120 ◽  
Author(s):  
Brandon Franzke ◽  
Bart Kosko
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Speed Up ◽  
Monte Carlo Estimation

scholarly journals Markov chain Monte Carlo estimation of quantiles

2014 ◽  
Vol 8 (2) ◽  
pp. 2448-2478 ◽  
Author(s):  
Charles R. Doss ◽  
James M. Flegal ◽  
Galin L. Jones ◽  
Ronald C. Neath
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Estimation

scholarly journals Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Heredity ◽  
2012 ◽  
Vol 109 (4) ◽  
pp. 235-245 ◽  
Author(s):  
B Mathew ◽  
A M Bauer ◽  
P Koistinen ◽  
T C Reetz ◽  
J Léon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Genetic Parameters ◽  
Monte Carlo Estimation ◽  
Estimation Of Genetic Parameters

scholarly journals Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

2017 ◽  
Author(s):  
Christopher De Sa ◽  
Kunle Olukotun ◽  
Christopher Ré
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gibbs Sampling ◽  
Mixing Time ◽  
Markov Chain Analysis ◽  
Empirical Results ◽  
Chain Analysis ◽  
Speed Up ◽  
Theoretical Results

Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating marginal distributions. To speed up Gibbs sampling, there has recently been interest in parallelizing it by executing asynchronously. While empirical results suggest that many models can be efficiently sampled asynchronously, traditional Markov chain analysis does not apply to the asynchronous case, and thus asynchronous Gibbs sampling is poorly understood. In this paper, we derive a better understanding of the two main challenges of asynchronous Gibbs: bias and mixing time. We show experimentally that our theoretical results match practical outcomes.

Sign in / Sign up

Export Citation Format

Share Document