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Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions.
Adaptive rejection sampling for Gibbs sampling
Gilks, Walter R and Wild, Pascal · 1992
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Neal, Radford M · 2001
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Estimation of population growth or decline in genetically monitored populations
Beaumont, Mark A · 2003
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Adaptive importance sampling in general mixture classes
Cappé, Olivier, Douc, Randal, Guillin, Arnaud, Marin, Jean-Michel, and Robert, Christian P · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
Andrieu, Christophe and Roberts, Gareth O · 2009
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MCMC using Hamiltonian dynamics
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Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte J, Schölkopf, Bernhard, and Smola, Alexander · 2012
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Adam: A method for stochastic optimization
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Variational inference: A review for statisticians
Blei, David M, Kucukelbir, Alp, and McAuliffe, Jon D · 2017
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Stein variational gradient descent as gradient flow
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Liu, Yang, Ramachandran, Prajit, Liu, Qiang, and Peng, Jian · 2017
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Liu, Qiang and Wang, Dilin · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Liu, Qiang, Lee, Jason D, and Jordan, Michael I · 2016
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Control functionals for monte carlo integration
Oates, Chris J, Girolami, Mark, and Chopin, Nicolas · 2017
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Advances in variational inference
Zhang, Cheng, Butepage, Judith, Kjellstrom, Hedvig, and Mandt, Stephan · 2017
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Chen, Wilson Ye, Mackey, Lester, Gorham, Jackson, Briol, François-Xavier, and Oates, Chris J · 2018
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