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Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions.
Patterns of Scalable Bayesian Inference
Angelino, E., Johnson, M. J., and Adams, R. P · 1935
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Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E · 1953
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Monte Carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
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Hybrid Monte Carlo
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D · 1987
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Inference from iterative simulation using multiple sequences
Gelman, A. and Rubin, D. B · 1992
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Slice sampling
Neal, R. M · 2003
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A tutorial on adaptive mcmc
Andrieu, C. and Thoms, J · 2008
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Adaptively scaling the metropolis algorithm using expected squared jumped distance
Pasarica, C. and Gelman, A · 2010
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Riemann manifold langevin and Hamiltonian Monte Carlo methods
Girolami, M. and Calderhead, B · 2011
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The no-u-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. D. and Gelman, A · 2011
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MCMC using Hamiltonian dynamics
Neal, R. M · 2011
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Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Transport map accelerated markov chain Monte Carlo
Parno, M. and Marzouk, Y · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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A conceptual introduction to Hamiltonian MOnte CArlo
Betancourt, M · 2017
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Stan: A probabilistic programming language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A · 2017
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Learning deep latent Gaussian models with Markov chain Monte CArlo
Hoffman, M. D · 2017
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Approximate inference with amortised MCMC
Li, Y., Turner, R. E., and Liu, Q · 2017
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Rapid mixing of Hamiltonian Monte Carlo on strongly log-concave distributions
Mangoubi, O. and Smith, A · 2017
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Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S · 2015
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Markov chain Monte Carlo and variational inference: Bridging the gap
Salimans, T., Kingma, D., and Welling, M · 2015
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Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., and Welling, M · 2016
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On the geometric ergodicity of Hamiltonian Monte Carlo
Livingstone, S., Betancourt, M., Byrne, S., and Girolami, M · 2016
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An introduction to sampling via measure transport
Marzouk, Y., Moselhy, T., Parno, M., and Spantini, A · 2016
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Generative adversarial learning of markov chains
Song, J., Zhao, S., and Ermon, S · 2017
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Learning model reparametrizations: Implicit variational inference by fitting mcmc distributions
Titsias, M. K · 2017
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On the quantitative analysis of decoder-based generative models
Wu, Y., Burda, Y., Salakhutdinov, R., and Grosse, R · 2017
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Hamiltonian variational auto-encoder
Caterini, A. L., Doucet, A., and Sejdinovic, D · 2018
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Generalizing Hamiltonian Monte Carlo with neural networks
Levy, D., Hoffman, M. D., and Sohl-Dickstein, J · 2018
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Ergodic measure preserving flows
Zhang, Y., Hernández-Lobato, J. M., and Ghahramani, Z · 2018
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