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Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling posteriors.
Nested sampling for general Bayesian computation
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Exponential convergence of Langevin distributions and their discrete approximations
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The Variational Formulation of the Fokker–Planck Equation
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Smart darting Monte Carlo
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Annealed importance sampling
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Density estimation by dual ascent of the log-likelihood
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Generalized darting Monte Carlo
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Markov chains and stochastic stability
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A family of nonparametric density estimation algorithms
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Dynamical Computation of the Density of States and Bayes Factors Using Nonequilibrium Importance Sampling
Rotskoff, G. M. and Vanden-Eijnden, E · 2019
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Solving Statistical Mechanics Using Variational Autoregressive Networks
Wu, D., Wang, L., and Zhang, P · 2019
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Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
Hartnett, G. S. and Mohseni, M · 2020
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Adaptive Monte Carlo augmented with normalizing flows
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