Error bounds for Approximations of Markov chains used in Bayesian Sampling
Publication
, Journal Article
Johndrow, JE; Mattingly, JC
November 14, 2017
We give a number of results on approximations of Markov kernels in total variation and Wasserstein norms weighted by a Lyapunov function. The results are applied to examples from Bayesian statistics where approximations to transition kernels are made to reduce computational costs.
Duke Scholars
Publication Date
November 14, 2017
Citation
APA
Chicago
ICMJE
MLA
NLM
Johndrow, J. E., & Mattingly, J. C. (2017). Error bounds for Approximations of Markov chains used in Bayesian
Sampling.
Johndrow, James E., and Jonathan C. Mattingly. “Error bounds for Approximations of Markov chains used in Bayesian
Sampling,” November 14, 2017.
Johndrow JE, Mattingly JC. Error bounds for Approximations of Markov chains used in Bayesian
Sampling. 2017 Nov 14;
Johndrow, James E., and Jonathan C. Mattingly. Error bounds for Approximations of Markov chains used in Bayesian
Sampling. Nov. 2017.
Johndrow JE, Mattingly JC. Error bounds for Approximations of Markov chains used in Bayesian
Sampling. 2017 Nov 14;
Publication Date
November 14, 2017