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Adaptive importance sampling is a powerful tool to sample from complicated target densities, but its success depends sensitively on the initial proposal density.
J. von Neumann, N. Metropolis, S. Ulam, Monte Carlo Method, National Bureau of Standards/Applied Math. Series 12 (1951) 36–38
1951
Earlier work this paper cites.
S. Kullback, R. Leibler, On information and sufficiency, Ann. Math. Stat. 22 (1) (1951) 79–86
1951
Earlier work this paper cites.
W. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1) (1970) 97–109
1970
Earlier work this paper cites.
H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control 19 (6) (1974) 716–723
1974
Earlier work this paper cites.
A. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. Ser. B Stat. Methodol. 39 (1) (1977) 1–38
1977
Earlier work this paper cites.
G. Schwarz, Estimating the dimension of a model, Ann. Stat. 6 (2) (1978) 461–464
1978
Earlier work this paper cites.
A. Gelman, D. Rubin, Inference from iterative simulation using multiple sequences, Stat. Sci. 7 (4) (1992) 457–472
1992
Earlier work this paper cites.
J. Liu, R. Chen, Blind deconvolution via sequential imputations, J. Amer. Statist. Assoc. 90 (430) (1995) 567–576
1995
Earlier work this paper cites.
G. O. Roberts, A. Gelman, W. R. Gilks, Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms, Ann. Appl. Probab. 7 (1) (1997) pp. 110–120
1997
Earlier work this paper cites.
H. Haario, E. Saksman, J. Tamminen, An Adaptive Metropolis Algorithm, Bernoulli 7 (2) (2001) pp. 223–242
2001
Earlier work this paper cites.
C. Robert, G. Casella, Monte Carlo statistical methods, Springer, 2004
2004
Earlier work this paper cites.
O. Cappé, A. Guillin, J.-M. Marin, C. P. Robert, Population Monte Carlo, J. Comput. Graph. Statist. 13 (4) (2004) 907–929
2004
Cited alongside, same era.
J. Goldberger, S. Roweis, Hierarchical clustering of a mixture model, Adv. Neur. Info. Proc. Syst. 17 (2004) 505
2004
Cited alongside, same era.
J. Skilling, Nested sampling for general Bayesian computation, Bayesian Analysis 1 (4) (2006) 833–860
2006
Cited alongside, same era.
O. Cappé, R. Douc, A. Guillin, J.-M. Marin, C. P. Robert, Adaptive importance sampling in general mixture classes, Stat. Comp. 18 (2008) 447–459
2008
Cited alongside, same era.
D. Wraith, M. Kilbinger, K. Benabed, O. Cappé, J.-F. Cardoso, et al., Estimation of cosmological parameters using adaptive importance sampling, Phys. Rev. D 80 (2009) 023507
2009
Cited alongside, same era.
G. Crooks, The Amoroso distribution (2010) · 2010
Later among the works it cites.
P. Bruneau, M. Gelgon, F. Picarougne, Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach, Patt. Recog. 43 (3) (2010) 850–858
2010
Later among the works it cites.
P. Giordani, R. Kohn, Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals, J. Comput. Graph. Statist. 19 (2) (2010) 243–259
2010
Later among the works it cites.
M. Kilbinger, K. Benabed, O. Cappé, J. Coupon, J.-F. Cardoso, G. Fort, H. J. McCracken, S. Prunet, C. P. Robert, D. Wraith, PMC lib v1.0 (2011). URL http://www2.iap.fr/users/kilbinge/CosmoPMC/
2011
Later among the works it cites.
F. Beaujean, C. Bobeth, D. van Dyk, C. Wacker, Bayesian Fit of Exclusive b → s ℓ ¯ ℓ b\to s\bar{\ell}\ell Decays: The Standard Model Operator Basis, JHEP 1208 (2012) 030
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F. Feroz, M. Hobson, M. Bridges, MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics, Mon. Not. Roy. Astron. Soc. 398 (2009) 1601–1614
2009
Cited alongside, same era.
A. El Attar, A. Pigeau, M. Gelgon, Fast aggregation of Student mixture models, in: European Signal Processing Conference (Eusipco 2009), 2009, pp. 312–216
2009
Cited alongside, same era.
2009
Cited alongside, same era.
S. S. AbdusSalam, B. C. Allanach, F. Quevedo, F. Feroz, M. Hobson, Fitting the phenomenological MSSM, Phys. Rev. D 81 (2010) 095012
2010
Cited alongside, same era.
M. Kilbinger, D. Wraith, C. P. Robert, K. Benabed, O. Cappe, et al., Bayesian model comparison in cosmology with Population Monte Carlo, Mon. Not. R. Astron. Soc 405 (4) (2010) 2381–2390
2010
Cited alongside, same era.
2012
Later among the works it cites.
F. Beaujean, A Bayesian analysis of rare B decays with advanced Monte Carlo methods , Dissertation, Technische Universität München (2012). URL http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:%91-diss-20121114-1115832-1-8
2012
Later among the works it cites.
L. Hoogerheide, A. Opschoor, H. K. van Dijk, A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation, J. Econometrics 171 (2) (2012) 101 – 120
2012
Later among the works it cites.
J.-M. Cornuet, J.-M. Marin, A. Mira, C. P. Robert, Adaptive Multiple Importance Sampling, Scand. J. Stat. 39 (4) (2012) 798–812
2012
Later among the works it cites.
2013
Closest in time.
The HDF5 group, Hierarchical data format version 5 (2013). URL http://www.hdfgroup.org/HDF5
2013
Closest in time.