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Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation.
Hybrid Monte Carlo
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D. (1987) · 1987
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Global Monte Carlo algorithms for many-fermion systems
Creutz, M. (1988) · 1988
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Bayesian learning via stochastic dynamics
Neal, R. M. (1993) · 1993
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Sequential imputation and Bayesian missing data problems
Kong, A., Liu, J. S., and Wong, W. H. (1994) · 1994
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Weak convergence and optimal scaling of random walk Metropolis algorithms
Roberts, G. O., Gelman, A., and Gilks, W. R. (1997) · 1997
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Optimal scaling of discrete approximations to Langevin diffusions
Roberts, G. O. and Rosenthal, J. S. (1998) · 1998
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Expectation propagation for approximate Bayesian inference
Minka, T. P. (2001) · 2001
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Annealed importance sampling
Neal, R. M. (2001) · 2001
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A sequential particle filter method for static models
Chopin, N. (2002) · 2002
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Geometric numerical integration illustrated by the Störmer–Verlet method
Hairer, E., Lubich, C., and Wanner, G. (2003) · 2003
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Scaling limits for the transient phase of local Metropolis–Hastings algorithms
Christensen, O. F., Roberts, G. O., and Rosenthal, J. S. (2005) · 2005
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Sequential Monte Carlo samplers
Del Moral, P., Doucet, A., and Jasra, A. (2006) · 2006
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Geometric numerical integration: structure-preserving algorithms for ordinary differential equations
Hairer, E., Lubich, C., and Wanner, G. (2006) · 2006
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Sequential Monte Carlo for Bayesian Computation
Del Moral, P., Doucet, A., and Jasra, A. (2007) · 2007
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A tutorial on adaptive MCMC
Andrieu, C. and Thoms, J. (2008) · 2008
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Marginal likelihood estimation via power posteriors
Friel, N. and Pettitt, A. N. (2008) · 2008
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Adaptively scaling the Metropolis algorithm using expected squared jumped distance
Pasarica, C. and Gelman, A. (2010) · 2010
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Girolami, M. and Calderhead, B. (2011) · 2011
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Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
Jasra, A., Stephens, D. A., Doucet, A., and Tsagaris, T. (2011) · 2011
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MCMC using Hamiltonian dynamics
Neal, R. M. (2011) · 2011
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Information Geometry and Sequential Monte Carlo
Sim, A., Filippi, S., and Stumpf, M. P. H. (2012) · 2012
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Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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Optimal tuning of the hybrid Monte Carlo algorithm
Beskos, A., Pillai, N., Roberts, G., Sanz-Serna, J.-M., and Stuart, A. (2013) · 2013
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Computational aspects of Bayesian spectral density estimation
An overview of the estimation of large covariance and precision matrices
Liu, H., Fan, J., and Liao, Y. (2016) · 2016
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On the geometric ergodicity of Hamiltonian Monte Carlo
Livingstone, S., Betancourt, M., Byrne, S., and Girolami, M. (2016) · 2016
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Parallel resampling in the particle filter
Murray, L. M., Lee, A., and Jacob, P. E. (2016) · 2016
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Computation of Gaussian orthant probabilities in high dimension
Ridgway, J. (2016) · 2016
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On the role of interaction in sequential Monte Carlo algorithms
Whiteley, N., Lee, A., and Heine, K. (2016) · 2016
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Toward Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach
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Chopin, N., Rousseau, J., and Liseo, B. (2013) · 2013
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An adaptive sequential Monte Carlo sampler
Fearnhead, P. and Taylor, B. M. (2013) · 2013
Cited alongside, same era.
Adaptive Hamiltonian and Riemann manifold Monte Carlo samplers
Mohamed, S., de Freitas, N., and Wang, Z. (2013) · 2013
Cited alongside, same era.
Sequential Monte Carlo on large binary sampling spaces
Schäfer, C. and Chopin, N. (2013) · 2013
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Optimizing the integrator step size for Hamiltonian Monte Carlo
Betancourt, M., Byrne, S., and Girolami, M. (2014) · 2014
Cited alongside, same era.
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. D. and Gelman, A. (2014) · 2014
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Measuring sample quality with Stein’s method
Gorham, J. and Mackey, L. (2015) · 2015
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Zhou, Y., Johansen, A. M., and Aston, J. A. (2016) · 2016
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Importance sampling: Intrinsic dimension and computational cost
Agapiou, S., Papaspiliopoulos, O., Sanz-Alonso, D., Stuart, A., et al. (2017) · 2017
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Stan: A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017) · 2017
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Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
Chopin, N. and Ridgway, J. (2017) · 2017
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Rapid mixing of Hamiltonian Monte Carlo on strongly log-concave distributions
Mangoubi, O. and Smith, A. (2017) · 2017
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Geometric integrators and the Hamiltonian Monte Carlo method
Bou-Rabee, N. and Sanz-Serna, J. M. (2018) · 2018
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Hamiltonian Sequential Monte Carlo with Application to Consumer Choice Behavior
Burda, M. and Daviet, R. (2018) · 2018
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Inference with Hamiltonian Sequential Monte Carlo Simulators
Daviet, R. (2018) · 2018
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Subsampling Sequential Monte Carlo for Static Bayesian Models
Gunawan, D., Kohn, R., Quiroz, M., Dang, K.-D., and Tran, M.-N. (2018) · 2018
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Sequential monte carlo as approximate sampling: bounds, adaptive resampling via ∞ \infty -ess, and an application to particle gibbs
Huggins, J. H. and Roy, D. M. (2018) · 2018
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Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?
Mangoubi, O., Pillai, N. S., and Smith, A. (2018) · 2018
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Unbiased and consistent nested sampling via sequential Monte Carlo
Salomone, R., South, L. F., Drovandi, C. C., and Kroese, D. P. (2018) · 2018
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Sequential Monte Carlo samplers with independent Markov chain Monte Carlo proposals
South, L. F., Pettitt, A. N., and Drovandi, C. C. (2019) · 2019
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