Computation of expectations by Markov chain Monte Carlo methods

Abstract

Markov chain Monte Carlo (MCMC) methods are a very versatile and widely used tool to compute integrals and expectations. In this short survey we focus on error bounds, rules for choosing the burn in, high dimensional problems and tractability versus curse of dimension.

14 pages. In: "Extraction of quantifiable information from complex systems", S. Dahlke et al. (eds.), Springer, 2014

References in corpus (3)

• Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
• Discrepancy estimates for variance bounding Markov chain quasi-Monte Carlo
• Error bounds of MCMC for functions with unbounded stationary variance

Cited by in corpus (5)

• Convergence of hybrid slice sampling via spectral gap
• Dimension-independent Markov chain Monte Carlo on the sphere
• Dimension-independent spectral gap of polar slice sampling
• Wasserstein contraction and spectral gap of slice sampling revisited
• Some Results on the Complexity of Numerical Integration