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Original Articles

Adaptive importance sampling in monte carlo integration

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Pages 143-168 | Received 12 Sep 1989, Published online: 18 May 2010

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Wenjia Wang, Yusi Fang, Chung Chang & George C. Tseng. (2023) Accurate and Ultra-Efficient p-Value Calculation for Higher Criticism Tests. Journal of Computational and Graphical Statistics 0:0, pages 1-14.
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Xiaoyu Xiong, Václav Šmídl & Maurizio Filippone. (2017) Adaptive multiple importance sampling for Gaussian processes. Journal of Statistical Computation and Simulation 87:8, pages 1644-1665.
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Václav Šmídl & Radek Hofman. (2014) Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation. Technometrics 56:4, pages 514-528.
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Jan C. Neddermeyer. (2009) Computationally Efficient Nonparametric Importance Sampling. Journal of the American Statistical Association 104:486, pages 788-802.
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O Cappé, A Guillin, J. M Marin & C. P Robert. (2004) Population Monte Carlo. Journal of Computational and Graphical Statistics 13:4, pages 907-929.
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Jae J. Lee & Steven C. Hillmer. (2003) Estimating the Effect of Parameter Uncertainty in Repeated Sample Surveys. Communications in Statistics - Simulation and Computation 32:2, pages 367-388.
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G. Jona‐ Lasinio, M. Piccioni & A. Ramponi. (1999) Selection of importance weights for monte carlo estimation of normalizing constants. Communications in Statistics - Simulation and Computation 28:2, pages 441-462.
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Ping Zhang. (1996) Nonparametric Importance Sampling. Journal of the American Statistical Association 91:435, pages 1245-1253.
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GeofH. Givens & AdrianE. Raftery. (1996) Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships. Journal of the American Statistical Association 91:433, pages 132-141.
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Man-Suk Oh & JamesO. Berger. (1993) Integration of Multimodal Functions by Monte Carlo Importance Sampling. Journal of the American Statistical Association 88:422, pages 450-456.
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Bin Liu & Chunlin Ji. (2013) A general algorithm scheme mixing computational intelligence with Bayesian simulation. A general algorithm scheme mixing computational intelligence with Bayesian simulation.
Lesław Gajek, Wojciech Niemiro & Piotr Pokarowski. (2013) Optimal Monte Carlo integration with fixed relative precision. Journal of Complexity 29:1, pages 4-26.
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Geof H. Givens & Jennifer A. Hoeting. 2012. Computational Statistics. Computational Statistics 423 455 .
Wei Chu, Martin Zinkevich, Lihong Li, Achint Thomas & Belle Tseng. (2011) Unbiased online active learning in data streams. Unbiased online active learning in data streams.
J. Siren, P. Marttinen & J. Corander. (2010) Reconstructing Population Histories from Single Nucleotide Polymorphism Data. Molecular Biology and Evolution 28:1, pages 673-683.
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Geneviève Lefebvre, Russell Steele & Alain C. Vandal. (2010) A path sampling identity for computing the Kullback–Leibler and J divergences. Computational Statistics & Data Analysis 54:7, pages 1719-1731.
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Surya T. Tokdar & Robert E. Kass. (2009) Importance sampling: a review. WIREs Computational Statistics 2:1, pages 54-60.
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Man-Suk Oh & Dai-Gyoung Kim. (2009) Efficiency and Robustness of Fully Adaptive Simulated Maximum Likelihood Method. Communications for Statistical Applications and Methods 16:3, pages 479-485.
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Claire Cannamela, Josselin Garnier & Bertrand Iooss. (2008) Controlled stratification for quantile estimation. The Annals of Applied Statistics 2:4.
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Julien Cornebise, Éric Moulines & Jimmy Olsson. (2008) Adaptive methods for sequential importance sampling with application to state space models. Statistics and Computing 18:4, pages 461-480.
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Suojin Wang. 2004. Encyclopedia of Statistical Sciences. Encyclopedia of Statistical Sciences.
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Micheal S Williams. (2001) Nonuniform random sampling: an alternative method of variance reductionfor forest surveys. Canadian Journal of Forest Research 31:12, pages 2080-2088.
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Leonid Peshkin & Sayan Mukherjee. 2001. Computational Learning Theory. Computational Learning Theory 616 629 .
Yun Bae Kim, Deok Seon Roh & Myeong Yong Lee. (2000) Nonparametric adaptive importance sampling for rare event simulation. Nonparametric adaptive importance sampling for rare event simulation.
K. Gerlach. (1999) New results in importance sampling [of false alarm statistics]. IEEE Transactions on Aerospace and Electronic Systems 35:3, pages 917-925.
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P.J. Smith, M. Shafi & Hongsheng Gao. (1997) Quick simulation: a review of importance sampling techniques in communications systems. IEEE Journal on Selected Areas in Communications 15:4, pages 597-613.
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Petros Dellaportas. (1995) Random variate transformations in the Gibbs sampler: issues of efficiency and convergence. Statistics and Computing 5:2, pages 133-140.
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J.S. Stadler & S. Roy. (1993) On the use of improved importance sampling (IIS) in the simulation of digital communication systems. On the use of improved importance sampling (IIS) in the simulation of digital communication systems.
Lennart F. Hoogerheide, H. K. van Dijk & R.D. van Oest. (2007) Simulation Based Bayesian Econometric Inference: Principles and Some Recent Computational Advances. SSRN Electronic Journal.
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