Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
arXiv: Learning, 2019.
We propose a new class of structured methods for Monte Carlo (MC) sampling, called DPPMC, designed for high-dimensional nonisotropic distributions where samples are correlated to reduce the variance of the estimator via determinantal point processes. We successfully apply DPPMCs to problems involving nonisotropic distributions arising i...More
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