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Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable distributions and integrals with respect to them.
New results on particle filters with adaptive number of particles
V. Elvira, J. Míguez, and P. M. Djurić · 1911
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Random sampling (Monte Carlo) techniques in neutron attenuation problems
H. Kahn · 1950
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Bayesian Inference in Statistical Analysis
G. E. P. Box and G. C. Tiao · 1973
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Numerical Methods That Work
F. S. Acton · 1990
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A note on importance sampling using standardized weights
A. Kong · 1992
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An Introduction to Applied Numerical Analysis
B. F. Plybon · 1992
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Novel approach to nonlinear and non-Gaussian Bayesian state estimation
N. Gordon, D. Salmond, and A. F. M. Smith · 1993
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Bayesian Theory
J. M. Bernardo and A. F. M. Smith · 1994
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Weighted average importance sampling and defensive mixture distributions
T. Hesterberg · 1995
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Optimally combining sampling techniques for Monte Carlo rendering
E. Veach and L. Guibas · 1995
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Numerical Analysis
R. L. Burden and J. D. Faires · 2000
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Rao-blackwellised particle filtering for dynamic Bayesian networks
A. Doucet, N. De Freitas, K. Murphy, and S. Russell · 2000
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Safe and effective importance sampling
A. Owen and Y. Zhou · 2000
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Auxiliary variable based particle filters
M. K. Pitt and N. Shephard · 2001
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Monte Carlo Methods in Finance
P. Jaeckel · 2002
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Gaussian particle filtering
J. Kotecha and P. M. Djurić · 2003
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Population Monte Carlo
O. Cappé, A. Guillin, J. M. Marin, and C. P. Robert · 2004
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Random Number Generation and Monte Carlo Methods
J. E. Gentle · 2004
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Handbook of Computational Methods for Integration
P. K. Kythe and M. R. Schaferkotter · 2004
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Monte Carlo Strategies in Scientific Computing
J. S. Liu · 2004
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Monte Carlo Statistical Methods
C. P. Robert and G. Casella · 2004
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Comparison of resampling schemes for particle filtering
R. Douc, O. Cappé, and E. Moulines · 2005
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Sequential Monte Carlo samplers
P. D. Moral, A. Doucet, and A. Jasra · 2006
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Multiple particle filtering
P. M. Djuric, T. Lu, and M. F. Bugallo · 2007
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Minimum variance importance sampling via population Monte Carlo
R. Douc, A. Guillin, J. M. Marin, and C. P. Robert · 2007
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The Bayesian Choice
C. P. Robert · 2007
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Adaptive importance sampling in general mixture classes
O. Cappé, R. Douc, A. Guillin, J. M. Marin, and C. P. Robert · 2008
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Truncated importance sampling
E. L. Ionides · 2008
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A tutorial on particle filtering and smoothing: Fifteen years later
A. Doucet and A. M. Johansen · 2009
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Importance sampling: a review
S. T. Tokdar and R. E. Kass · 2010
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Exploring Monte Carlo Methods
W. L. Dunn and J. K. Shultis · 2011
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Handbook of Monte Carlo Methods
D. Kroese, T. Taimre, and Z. Botev · 2011
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Adaptive multiple importance sampling
J. M. Cornuet, J. M. Marin, A. Mira, and C. P. Robert · 2012
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Monte Carlo theory, methods and examples
A. B. Owen · 2013
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Quadrature and numerical integration
M. C. Ausín · 2014
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Gibbs sampling
J. A. Christen · 2014
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Optimal combination of techniques in multiple importance sampling
V. Havran and M. Sbert · 2014
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Optimal mixture weights in multiple importance sampling
H. Y. He and A. B. Owen · 2014
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Importance sampling
A. Kong · 2014
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Bayesian inference
P. M. Lee · 2014
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An adaptive population importance sampler
L. Martino, V. Elvira, D. Luengo, and J. Corander · 2014
The sample size required in importance sampling
S. Chatterjee, P. Diaconis, et al · 2018
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Robust covariance adaptation in adaptive importance sampling
Y. El-Laham, V. Elvira, and M. F. Bugallo · 2018
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In search for improved auxiliary particle filters
V. Elvira, L. Martino, M. F. Bugallo, and P. M. Djurić · 2018
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Langevin incremental mixture importance sampling
M. Fasiolo, F. E. de Melo, and S. Maskell · 2018
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On exploration, exploitation and learning in adaptive importance sampling
X. Lu, T. Rainforth, Y. Zhou, J.-W. van de Meent, and Y. W. Teh · 2018
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Efficient linear fusion of partial estimators
D. Luengo, L. Martino, V. Elvira, and M. Bugallo · 2018
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Improving smc sampler estimate by recycling all past simulated particles
T. L. T. Nguyen, F. Septier, G. W. Peters, and Y. Delignon · 2014
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Monte Carlo methods
C. P. Robert · 2014
Cited alongside, same era.
Adaptive importance sampling via stochastic convex programming
E. K. Ryu and S. P. Boyd · 2014
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Importance sampling including the bootstrap
S. Wang · 2014
Cited alongside, same era.
A gradient adaptive population importance sampler
V. Elvira, L. Martino, L. Luengo, and J. Corander · 2015
Cited alongside, same era.
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Compressed Monte Carlo for distributed Bayesian inference
L. Martino and V. Elvira · 2018
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A comparison of clipping strategies for importance sampling
L. Martino, V. Elvira, J. Míguez, A. Artés-Rodríguez, and P. Djurić · 2018
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Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models
J. Miguez, I. P. Mariño, and M. A. Vázquez · 2018
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Multiple importance sampling revisited: breaking the bounds
M. Sbert, V. Havran, and L. Szirmay-Kalos · 2018
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Convergence rates for optimised adaptive importance samplers
Ö. D. Akyildiz and J. Míguez · 2019
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Compressed streaming importance sampling for efficient representations of localization distributions
A. S. Bedi, A. Koppel, V. Elvira, and B. M. Sadler · 2019
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Recursive shrinkage covariance learning in adaptive importance sampling
Y. El-Laham, V. Elvira, and M. F. Bugallo · 2019
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Efficient adaptive multiple importance sampling
Y. El-Laham, L. Martino, V. Elvira, and M. F. Bugallo · 2019
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Langevin-based strategy for efficient proposal adaptation in population Monte Carlo
V. Elvira and É. Chouzenoux · 2019
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Efficient ser estimation for mimo detectors via importance sampling schemes
V. Elvira and I. Santamaría · 2019
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Gauss-Hermite quadrature for non-gaussian inference via an importance sampling interpretation
V. Elvira, P. Closas, and L. Martino · 2019
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Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via i n f t y infty -ess, and an application to particle gibbs
J. H. Huggins, D. M. Roy, et al · 2019
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Revisiting the balance heuristic for estimating normalising constants
F. J. Medina-Aguayo and R. G. Everitt · 2019
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Importance sampling the union of rare events with an application to power systems analysis
A. B. Owen, Y. Maximov, M. Chertkov, et al · 2019
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Optimal deterministic mixture sampling
M. Sbert, V. Havran, and L. Szirmay-Kalos · 2019
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Monte Carlo methods
T. Taimre, D. P. Kroese, and Z. I. Botev · 2019
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Optimized auxiliary particle filters
N. Branchini and V. Elvira · 2020
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Importance gaussian quadrature
V. Elvira, L. Martino, and P. Closas · 2020
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A survey of monte carlo methods for parameter estimation
D. Luengo, L. Martino, M. Bugallo, V. Elvira, and S. Särkkä · 2020
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Optimized population monte carlo
V. Elvira and E. Chouzenoux · 2021
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Multiple importance sampling for symbol error rate estimation of maximum-likelihood detectors in mimo channels
V. Elvira and I. Santamaria · 2021
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Nearly consistent finite particle estimates in streaming importance sampling
A. Koppel, A. S. Bedi, B. M. Sadler, and V. Elvira · 2021
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Compressed monte carlo with application in particle filtering
L. Martino and V. Elvira · 2021
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Compressed particle methods for expensive models with application in astronomy and remote sensing
L. Martino, V. Elvira, J. López-Santiago, and G. Camps-Valls · 2021
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Hamiltonian adaptive importance sampling
A. Mousavi, R. Monsefi, and V. Elvira · 2021
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Rethinking the effective sample size
V. Elvira, L. Martino, and C. P. Robert · 2022
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Generalizing the balance heuristic estimator in multiple importance sampling
M. Sbert and V. Elvira · 2022
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