Simulation and the Monte Carlo Method

By J. Carson. 1982

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Citations in the corpus

Combining Kernel and Model Based Learning for HIV Therapy Selection (2017, background): “• Weighted Importance Sampling [20] attempts to reduce this variance by weighting the importance scores.”
Batch Stationary Distribution Estimation (2020, background): “Standard importance weighting is known to have high variance, and various techniques have been proposed to reduce variance (Precup et al., 2001; Jiang and Li, 2016; Rubinstein and Kroese, 2016; Thomas and Brunskill, 2016; Guo et al., 2017).”
Diversity Thresholds: How Social Norms, Visibility, and Scrutiny Relate to Group Composition (2019, methodology): “We determined the expected distribution using a Monte Carlo simulation method (Rubinstein & Kroese, 2011).”
A RAD approach to deep mixture models (2019, background): “This can be seen as the inferential converse of a stratified sampling strategy (Rubinstein and Kroese, 2016).”
A reference Earth model for the heat‐producing elements and associated geoneutrino flux (2013, methodology): “[ 65 ] To trace the error propagation in our reference model, we used MATLAB to perform a Monte Carlo simulation [ Huang et al ., 2013; Robert and Casella , 2004; Rubinstein and Kroese , 2008].”
PENELOPE-2006: A Code System for Monte Carlo Simulation of Electron and Photon Transport (2009, methodology): “Under special circumstances, the statistical uncertainties may be lowered by using variance-reduction techniques (Rubinstein, 1981; Bielajew and Rogers, 1988).”
Mixture Kalman filters (2000, methodology): “Thus, by using the marginalization technique for Monte Carlo computation (Rubinstein, 1981), we derive a Monte Carlo lter that focuses its full attention on 2”
VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES (2018, unknown): “The closest methods to our approach are the adaptive importance sampling methods where data collected from an initial proposal distribution is iteratively used to adjust it to maximize sampling efﬁciency (e.g., Rubinstein 1997; Rubinstein & Kroese 2017; Li et al. 2013).”
Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation (2002, unknown): “͑2͒ is not feasible for existing methods such as Monte Carlo simulation ͑MCS͒ or importance sampling ͑Rubinstein 1981͒.”
The Shapley Value in Machine Learning (2022, background): “Mitchell et al. [2021] show that other Monte Carlo variance reduction techniques can also be applied to this problem, such as antithetic sampling [Lomeli et al. , 2019; Rubinstein et al. , 2016].”