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Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling.
Convergence rates for optimised adaptive importance samplers
Akyildiz, Ö. D. and Míguez, J. (2019) · 1903
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Generalizing the balance heuristic estimator in multiple importance sampling
Sbert, M. and Elvira, V. (2019) · 1903
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Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C. (2019b) · 1903
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Rational decisions
Good, I. (1952) · 1952
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Methods of reducing sample size in Monte Carlo computations
Kahn, H. and Marshall, A. W. (1953) · 1953
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Expected information as expected utility
Bernardo, J. M. (1979) · 1979
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A predictive approach to model selection
Geisser, S. and Eddy, W. F. (1979) · 1979
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Advances in importance sampling
Hesterberg, T. C. (1988) · 1988
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Model determination using predictive distributions with implementation via sampling-based methods (with discussion)
Gelfand, A. E., Dey, D. K., and Chang, H. (1992) · 1992
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A note on importance sampling using standardized weights
Kong, A. (1992) · 1992
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Bayesian theory
Bernardo, J. M. and Smith, A. F. (1994) · 1994
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Weighted average importance sampling and defensive mixture distributions
Hesterberg, T. (1995) · 1995
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Optimally combining sampling techniques for Monte Carlo rendering
Veach, E. and Guibas, L. J. (1995) · 1995
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Model determination using sampling-based methods
Gelfand, A. E. (1996) · 1996
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Nonparametric importance sampling
Zhang, P. (1996) · 1996
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On the variability of case-deletion importance sampling weights in the Bayesian linear model
Peruggia, M. (1997) · 1997
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Bayesian model averaging: a tutorial
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999) · 1999
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Comparative hybridization of an array of 21 500 ovarian cdnas for the discovery of genes overexpressed in ovarian carcinomas
Schummer, M., Ng, W. V., Bumgarner, R. E., Nelson, P. S., Schummer, B., Bednarski, D. W., Hassell, L., Baldwin, R. L., Karlan, B. Y., and Hood, L. (1999) · 1999
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Safe and effective importance sampling
Owen, A. and Zhou, Y. (2000) · 2000
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Warp bridge sampling
Meng, X.-L. and Schilling, S. (2002) · 2002
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Bayesian model assessment and comparison using cross-validation predictive densities
Vehtari, A. and Lampinen, J. (2002) · 2002
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Population Monte Carlo
Cappé, O., Guillin, A., Marin, J.-M., and Robert, C. P. (2004) · 2004
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General state space Markov chains and MCMC algorithms
Roberts, G. O., Rosenthal, J. S., et al. (2004) · 2004
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Data analysis using regression and multilevel/hierarchical models
Gelman, A. and Hill, J. (2006) · 2006
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
Cited alongside, same era.
Adaptive importance sampling in general mixture classes
Cappé, O., Douc, R., Guillin, A., Marin, J.-M., and Robert, C. P. (2008) · 2008
Cited alongside, same era.
Case-deletion importance sampling estimators: Central limit theorems and related results
Epifani, I., MacEachern, S. N., and Peruggia, M. (2008) · 2008
Cited alongside, same era.
Heretical multiple importance sampling
Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2016) · 2016
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Adaptive importance sampling for control and inference
Kappen, H. J. and Ruiz, H. C. (2016) · 2016
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Variance analysis of multi-sample and one-sample multiple importance sampling
Sbert, M., Havran, V., and Szirmay-Kalos, L. (2016) · 2016
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A Conceptual Introduction to Hamiltonian Monte Carlo
Betancourt, M. (2017) · 2017
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Adaptive importance sampling: the past, the present, and the future
Bugallo, M. F., Elvira, V., Martino, L., Luengo, D., Miguez, J., and Djuric, P. M. (2017) · 2017
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Stan: A probabilistic programming language
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Truncated importance sampling
Ionides, E. L. (2008) · 2008
Cited alongside, same era.
Rare event simulation using Monte Carlo methods
Rubino, G. and Tuffin, B. (2009) · 2009
Cited alongside, same era.
Predictive likelihood for Bayesian model selection and averaging
Ando, T. and Tsay, R. (2010) · 2010
Cited alongside, same era.
Expectation propagation for microarray data classification
Hernández-Lobato, D., Hernández-Lobato, J. M., and Suárez, A. (2010) · 2010
Cited alongside, same era.
Adaptive multiple importance sampling
Cornuet, J.-M., Marin, J.-M., Mira, A., and Robert, C. P. (2012) · 2012
Cited alongside, same era.
A survey of Bayesian predictive methods for model assessment, selection and comparison
Vehtari, A. and Ojanen, J. (2012) · 2012
Cited alongside, same era.
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017) · 2017
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Improving population Monte Carlo: Alternative weighting and resampling schemes
Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2017) · 2017
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Effective sample size for importance sampling based on discrepancy measures
Martino, L., Elvira, V., and Louzada, F. (2017) · 2017
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Adaptive multiple importance sampling for general functions
Sbert, M. and Havran, V. (2017) · 2017
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Practical Bayesian model evaluation using leave-one-out cross-validation and waic
Vehtari, A., Gelman, A., and Gabry, J. (2017) · 2017
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The sample size required in importance sampling
Chatterjee, S., Diaconis, P., et al. (2018) · 2018
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Rethinking the effective sample size
Elvira, V., Martino, L., and Robert, C. P. (2018) · 2018
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Uniform convergence of sample average approximation with adaptive multiple importance sampling
Feng, M. B., Maggiar, A., Staum, J., and Wächter, A. (2018) · 2018
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Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models
Miguez, J., Mariño, I. P., and Vázquez, M. A. (2018) · 2018
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Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation
Rischard, M., Jacob, P. E., and Pillai, N. (2018) · 2018
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RStan: the R interface to Stan, version 2.17.3
Stan Development Team (2018) · 2018
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Generalized multiple importance sampling
Elvira, V., Martino, L., Luengo, D., Bugallo, M. F., et al. (2019) · 2019
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A swiss army infinitesimal jackknife
Giordano, R., Stephenson, W., Liu, R., Jordan, M., and Broderick, T. (2019) · 2019
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Probabilistic forecasting and comparative model assessment based on Markov chain Monte Carlo output
Krueger, F., Lerch, S., Thorarinsdottir, T. L., and Gneiting, T. (2019) · 2019
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R: A Language and Environment for Statistical Computing
R Core Team (2020) · 2020
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