Stochastic Simulation: Algorithms and Analysis

By Søren Asmussen, Peter W. Glynn. 2007

1,360 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Evaluation & Benchmarks

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Citations in the corpus

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018, methodology): “We refer to, for example, Asmussen and Glynn (2010) and Law (2014) for further details on data sampling and simulations.”
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation (2019, background): “…robustness for off-policy evaluation, orthogonal to the existing literature of doubly robust policy evaluation that solely devotes to variance reduction (Jiang & Li, 2016; Thomas & Brunskill, 2016; Farajtabar et al., 2018), mostly based on the idea of control variates (e.g. Asmussen & Glynn, 2007).”
DGM: A deep learning algorithm for solving partial differential equations (2017, positive): “This approximation error can be further improved via the following scheme using “antithetic variates”: The approximation (3.2) has O (∆) bias as an approximation for See [1] for a background on antithetic variates in simulation algorithms.”
Decoupling the Short- and Long-Term Behavior of Stochastic Volatility (2016, methodology): “What is more, the possibility of non-Gaussianity of the BSS process poses further problems, as this rules out simulation methods based on Gaussianity, such as Cholesky factorization and circulant embedding methods (e.g., Asmussen and Glynn, 2007, Chapter XI).”
A Stochastic Quasi-Newton Method for Large-Scale Optimization (2014, background): “Although problem (1.1) arises other in settings, such as simulation optimization [2], we assume for concreteness that ξ is a random instance consisting of an input-output pair ( x, z ).”
Tempered stable distributions and processes in finance: numerical analysis (2010, background): “This series representation is not new in the literature, see [2, 12].”
Quantification of model uncertainty in RANS simulations: A review (2018, background): “Uncertainty propagation (Forward analysis) Techniques to propagate uncertainties can be classified into two categories: spectral methods [60] and Monte Carlo (MC) methods [61].”
An Academic Response to Basel 3.5 (2014, methodology): “For α close to 1, tools from rare event simulation become important; see for instance Asmussen and Glynn (2007) (Chapter VI).”
Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations (2014, background): “Simulation algorith ms for Gaussian processes are presented in [6].”
Fast Random Integer Generation in an Interval (2018, methodology): “1For some applications in which streams of random numbers need to be synchronized, the rejection method is not applicable [2, 4, 12, 19, 23].”
Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs (2018, background): “Additionally, burnin phases do not change the asymptotic posterior, and additionally can help the rate of convergence in convex stochastic optimization [14, 1].”