Warp Bridge Sampling

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An introduction to sampling via measure transport (2016, background): “[56] introduces several deterministic and/or stochastic transformations to increase the overlap of the two densities—by translating, scaling, and even symmetrizing them.”
Control variates for quasi-Monte Carlo (2005, background): “(2.2) Note that in common IS settings, as in HLO,c2 is chosen to be 1 and thusr = c1, but in more general settings ratios are of interest; see Meng and Schilling (2002) for a recent discussion of this issue.”
Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “In brms, this is realized via bridge sampling (Meng and Wong 1996; Meng and Schilling 2002) as implemented in the bridgesampling package (Gronau et al.”
MCMC Using Hamiltonian Dynamics (2011, background): “which involves the unknown ratio of two normalizing constants, Cθ/Cθ′, a problem which occurs in many areas (see for example [29], [11], and [28]).”