# Dimension-independent likelihood-informed MCMC

@article{Cui2016DimensionindependentLM, title={Dimension-independent likelihood-informed MCMC}, author={Tiangang Cui and Kody J. H. Law and Youssef M. Marzouk}, journal={J. Comput. Physics}, year={2016}, volume={304}, pages={109-137} }

- Published 2016 in J. Comput. Physics
DOI:10.1016/j.jcp.2015.10.008

Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that, in principle, can be described as functions. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on… CONTINUE READING

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