Is it meaningful to talk about “generic” Monte Carlo variance reduction strategies? Let us create this notebook and see if accretes some references.
For now there are a couple of links about optimal sample diversity. There are other strategies available, such as Rao-Blackwellisation.
Mariet, Zelda Elaine. 2016. “Learning and enforcing diversity with Determinantal Point Processes.” Thesis, Massachusetts Institute of Technology.
Roberts, Gareth O., and Jeffrey S. Rosenthal. 2014. “Minimising MCMC Variance via Diffusion Limits, with an Application to Simulated Tempering.” Annals of Applied Probability 24 (1): 131–49.
Sheikh, Hassam Ullah, Mariano Phielipp, and Ladislau Boloni. 2022. “Maximizing Ensemble Diversity in Deep Reinforcement Learning,” 25.
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