Random Number Generation and Monte Carlo Methods

By C. Borkowf. Technometrics, 2000

751 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/861767344a01ca161862ac31fc721f6a2f58417a

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In Statistical and Nonlinear Physics

Citations in the corpus

Dynamics of Consumer Demand for New Durable Goods (2012, neutral): “This integration is further described in Appendix C.”
The Algorithm Design Manual (2020, background): “That said, see [Gen04] for more recent developments in random number generation.”
Design of computer experiments: A review (2017, background): “This has motivated efforts to develop a variety of pseudorandom number generators [66].”
Elephant spatial use in wet and dry savannas of southern Africa (2009, methodology): “We permutated this procedure 10 000 times (see Gentle, 1943 ) and proposed a null model that the mean value of visited grid cells was drawn from the same distribution as values from randomly selected grid cells within the same 10 day period.”
Bayesian evidence test for precise hypotheses (2003, background): “The reader is referred to Gentle (2000) , Liu (2001), Luenberger (1984), Ljung et al. (1992), McCormick (1983), Q”
Probabilistic load flow in systems with wind generation (2009, methodology): “The most straightforward method of solving this problem is Monte Carlo simulation [4].”
Automatic Nonuniform Random Variate Generation (2007, neutral): “However, for general use in a graduate course on random variate generation and simulation methods, a text such as that of Gentle (1998) probably would be more appropriate.”
Coordinated Exploration in Concurrent Reinforcement Learning (2018, background): “, ad, and (b) any Gamma with shape parameter a can be represented as the sum of a Exp(1) random variables (Gentle, 2013).”
A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects (pp.188) (2010, methodology): “the gamma model (Gentle, 2003).”
Order-Preserving Symmetric Encryption (2009, background): “What is known is that the NHG distribution can be approximated by the negative binomial distribution [24], the latter can be sampled efficiently [16,14], and the approximation improves as M and N grow.”
Discrete-time approximations of stochastic delay equations: The Milstein scheme (2004, neutral): “[12], Section 3.”
MCDB: a monte carlo approach to managing uncertain data (2008, methodology): “We can, however, go far beyond graphical displays: the power of MCDB lies in the fact that we can leverage over 50 years of Monte Carlo technology [17, 21] to make statistical inferences about the distribution ofX , about interesting features of this distribution such as means and quantiles, and abo”
Statistical model of lossy links in wireless sensor networks (2005, methodology): “While the simplest models are generated using standard rejection random number generator approach [7], for realization of more complex models we have developed an iterative improvement-based procedure.”
Second-order Monte Carlo uncertainty/variability analysis using correlated model parameters: application to salmonid embryo survival risk assessment (2004, methodology): “, yk)T, I is the k × k identity matrix, and |RN | is the determinant of RN . The normal copula density given in Eq. (A.7) can be used to generate correlated random numbers by an acceptance/rejection procedure ( Gentle, 1998; Haas, 1999), as described below:”
Lifetime Estimation of High-Power White LED Using Degradation-Data-Driven Method (2012, methodology): “918∗e−6) with S-PLUS (one software for statistical computing from Bell Laboratories) random number generation function [17].”