Cited by 3,356Simulation Techniques and ApplicationsMarkov Chains and Monte Carlo MethodsMathematical Approximation and Integration

The DOI matches its Crossref record.

Simulation and the Monte Carlo Method

Rubinstein, Reuven Y., Kroese, Dirk P.·2016·Wiley Series in Probability and Statistics

DOI 10.1002/9781118631980

Open this paper in CoChat — free account

Abstract

Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences." "Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.

Integrity signals

Registry

Crossref · 10.1002/9781118631980

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 701–800 of 1,003

Global multipath Monte Carlo algorithms for radiosity
Sbert, M., Pueyo, X., Neumann, L., et al. (1996) · The Visual Computer · doi:10.1007/bf01782104 · Cited by 37
Performance analysis of a medium concentrated photovoltaic system thermally regulated by phase change material: Phase change material selection and comparative analysis for different climates
Abbas, Q., Sarwar, J., Arafat, S. M., et al. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.121653 · Cited by 36
Physics-informed neural network classification framework for reliability analysis
Shi, Y., & Beer, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125207 · Cited by 38
A dynamic risk-informed framework for emergency human error prevention in high-risk industries: A Nuclear Power Plant case study
Xiao, X., Qi, B., Liu, S., Chen, P., Liang, J., Tong, J., & Wang, H. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111080 · Cited by 37
Probabilistic analysis of consolidation with prefabricated vertical drains for soil improvement
HONG, H. P., & Shang, J. Q. (1998) · Canadian Geotechnical Journal · doi:10.1139/t98-031 · Cited by 36
Blood Pressure Variability Causes Spurious Identification of Hypertension in Clinical Studies: A Computer Simulation Study
Turner, M. J., & van Schalkwyk, J. M. (2007) · American Journal of Hypertension · doi:10.1038/ajh.2007.25 · Cited by 36
Probabilistic congestion management using EVs in a smart grid with intermittent renewable generation
Romero-Ruiz, J., Pérez‐Ruiz, J., Martín, S., et al. (2016) · Electric Power Systems Research · doi:10.1016/j.epsr.2016.03.015 · Cited by 36
COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management
Patelli, E. (2017) · Springer eBooks · doi:10.1007/978-3-319-12385-1_59 · Cited by 36
Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia)
Chartin, C., Stevens, A., Goidts, E., Krüger, I., Carnol, M., & van Wesemael, B. (2017) · Geoderma Regional · doi:10.1016/j.geodrs.2016.12.006 · Cited by 35
“4) were used (Hammersley and Handscomb, 1964; Rubinstein, 1981).”
Structural reliability using monte-carlo simulation with variance reduction techniques on elastic-plastic structures
Pulido, J., Jacobs, T. L., & Lima, E. C. P. (1992) · Computers & Structures · doi:10.1016/0045-7949(92)90275-5 · Cited by 36
Multiphase flow in deforming porous material
Schrefler, B. A., & Pesavento, F. (2004) · Computers and Geotechnics · doi:10.1016/j.compgeo.2004.01.005 · Cited by 36
Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment
Uchibe, E., & Asada, M. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.876918 · Cited by 36
“Although their approach is efficient and simple from a viewpoint of evolutionary computation, a single population is not a natural assumption in a competitive situation.”
Background
Mechanistic Insights in the Catalytic Synthesis of Vinyl Acetate on Palladium and Gold/Palladium Alloy Surfaces
Neurock, M., & Tysoe, W. T. (2013) · Topics in Catalysis · doi:10.1007/s11244-013-0153-8 · Cited by 36
Analog circuit simulation using range arithmetics
Grabowski, D., Olbrich, M., & Barke, E. (2008) · doi:10.5555/1356802.1356984 · Cited by 36
Water Distribution Network Reliability: Stochastic Simulation
Yang, S., Hsu, N., Louie, P. W. F., et al. (1996) · Journal of Infrastructure Systems · doi:10.1061/(asce)1076-0342(1996)2:2(65 · Cited by 36
A variance reduction framework for stable feature selection
Han, Y., & Lei, Y. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11152 · Cited by 36
“Our theoretical framework reveals the relationship between the stability and the accuracy of feature selection algorithms, where stability is measured w.r.t. the results (feature weights or subsets) of a feature selection algorithm from different training sets.”
Background
Transient responses of dynamical systems with random uncertainties
Soize, C. (2001) · Probabilistic Engineering Mechanics · doi:10.1016/s0266-8920(01)00026-1 · Cited by 37
Reliability analysis with cross-entropy based adaptive Markov chain importance sampling and control variates
Mehni, M. B., Mehni, M. B., Mehni, M. B., et al. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.109014 · Cited by 35
Solving stochastic optimal reactive power dispatch using an Adaptive Beluga Whale optimization considering uncertainties of renewable energy resources and the load growth
Ebeed, M., Ali, S. M., Kassem, A. M., et al. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102762 · Cited by 35
High dimensional model representation for stochastic finite element analysis
Chowdhury, R., & Adhikari, S. (2010) · Applied Mathematical Modelling · doi:10.1016/j.apm.2010.04.004 · Cited by 35
“Methods involving perturbation expansion [8], Neumann series expansion [9], first- and second-order reliability algorithms [10–12], and Monte Carlo simulation (MCS) [13] including variance reduction strategies have been developed and extensively used for probabilistic analysis of complex structures.”
Machine Learning Inspired Hybrid Precoding for Wideband Millimeter-Wave Massive MIMO Systems
Mir, T., Siddiqi, M. Z., Mir, U., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2916883 · Cited by 35
Revisiting the importance of appropriate parameter estimation based on sensitivity analysis for developing kinetic models
Sámano, V., Tirado, A., Félix, G., et al. (2020) · Fuel · doi:10.1016/j.fuel.2020.117113 · Cited by 35
Robust Design Optimization of SPMSM for Robotic Actuator Considering Assembly Imperfection of Segmented Stator Core
Lee, S., Kim, S., Park, J.-C., et al. (2020) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2020.2999127 · Cited by 35
Extension of AK-MCS for the efficient computation of very small failure probabilities
Razaaly, N., & Congedo, P. M. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107084 · Cited by 35
“Sampling methods include the well-known Monte-Carlo (MC) characterized by a low convergence rate, Importance Sampling (IS) [4, 5] relying on a prudent choice of the importance sampling density and Subset Simulation (SS) [6].”
Method
Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process
Tsai, Y., Lee, C.-H., Liu, T.-Y., et al. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.07.001 · Cited by 35
Monte Carlo Methods and Importance Sampling
Anderson, E. C. (1999) · Cited by 35
Exploration in Gradient-Based Reinforcement Learning
Meuleau, N., Peshkin, L., & Kim, K.-E. (2001) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 35
A methodological approach for the identification and quantification of sources of biological variance in postharvest research
Ketelaere, B. D., Stulens, J., Lammertyn, J., Cuong, N. V., & Baerdemaeker, J. D. (2005) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2005.09.004 · Cited by 35
Antithetic Variates, Multivariate Dependence and Simulation of Stochastic Systems
Rubinstein, R. Y., Samorodnitsky, G. P., & Shaked, M. (1985) · Management Science · doi:10.1287/mnsc.31.1.66 · Cited by 35
Self-citation
Generation of poisson and gamma random vectors with given marginals and covariance matrix
Sim, C. H. (1993) · Journal of Statistical Computation and Simulation · doi:10.1080/00949659308811506 · Cited by 35
flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX
Wong, K. W. K., Gabrié, M., & Foreman-Mackey, D. T. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05021 · Cited by 34
An efficient and versatile Kriging-based active learning method for structural reliability analysis
Wang, J., Xu, G., Yuan, P., et al. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109670 · Cited by 34
Machine Learning in Soft Matter: From Simulations to Experiments
Zhang, K., Gong, X., & Jiang, Y. L. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202315177 · Cited by 34
Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects
Rahman, M. A., Islam, M. R., Hossain, M. J., Raña, M. S., Hossain, M. J., & Gray, E. M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108785 · Cited by 34
Stochastic multi-layer optimization for cooperative multi-microgrid systems with hydrogen storage and demand response
Alamir, N., Kamel, S. M., & Abdelkader, S. M. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.12.244 · Cited by 34
Fixed head short-term hydrothermal scheduling in presence of solar and wind power
Das, S., Bhattacharya, A., & Chakraborty, A. K. (2018) · Energy Strategy Reviews · doi:10.1016/j.esr.2018.08.001 · Cited by 34
European supply chains for carbon capture, transport and sequestration, with uncertainties in geological storage capacity: Insights from economic optimisation
d’Amore, F., Sunny, N., Iruretagoyena, D., Bezzo, F., & Shah, N. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.106521 · Cited by 35
Robustness of Short-Term Wind Power Forecasting against False Data Injection Attacks
Zhang, Y., Fan, L., & Wang, K. (2020) · Energies · doi:10.3390/en13153780 · Cited by 34
Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation
Masri, M. E., Morio, J., & Simatos, F. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107991 · Cited by 34
“Sampling techniques based on Monte Carlo simulations take advantage of the law of large numbers for rare event probability estimations [4], where this probability is estimated as the average number of random samples which fall in the failure domain.”
BackgroundMethod
An overview on multi-carrier energy networks: From a concept to future trends and challenges
Shabanpour-Haghighi, A., & Karimaghaei, M. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2021.11.257 · Cited by 34
Probabilistic Based Reliability Slope Stability Analysis Using FOSM, FORM, and MCS
Kar, S. S., & Roy, L. B. (2022) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.4689 · Cited by 34
An active-learning reliability method based on support vector regression and cross validation
Zhou, T., & Peng, Y. (2022) · Computers & Structures · doi:10.1016/j.compstruc.2022.106943 · Cited by 34
Carbon footprint of the globe artichoke supply chain in Southern Italy: From agricultural production to industrial processing
Rana, R. L., Bux, C., & Lombardi, M. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136240 · Cited by 34
Power system reliability and its assessment. Part 2: Composite generation and transmission systems
Allan, R. N., & Billinton, R. (1992) · Power Engineering Journal · doi:10.1049/pe:19920058 · Cited by 34
Reliability-based design of sight distance at railroad grade crossings
Easa, S. M. (1994) · Transportation Research Part A Policy and Practice · doi:10.1016/0965-8564(94)90038-8 · Cited by 34
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment
Abbas, I., Rovira, J., & Casanovas, J. (2006) · Contemporary Clinical Trials · doi:10.1016/j.cct.2006.08.002 · Cited by 33
“These methods are well documented [9–14] and were applied jointly and singly.”
Method
Approximating the probability density function of the optimal point of an optimization problem
Lopez, R. H., de Cursi, E. S., & Lemosse, D. (2010) · Engineering Optimization · doi:10.1080/0305215x.2010.489607 · Cited by 34
“(16) The validation of the method is made by comparing its results with those obtained through the MCS. Simulations methods using sampling and estimation are well known in the literature, Direct Monte Carlo simulations (MCS) being the most widely used (Rubinstein 1981).”
Method
Observations on Limit Equilibrium–Based Slope Reliability Problems with Inclined Weak Seams
Ching, J., Phoon, K., & Hu, Y.-G. (2010) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0000160 · Cited by 34
Voltage collapse risk assessment
da Silva, A. M. L., Coutinho, I., de Souza, A. C. Z., et al. (2000) · Electric Power Systems Research · doi:10.1016/s0378-7796(99)00087-5 · Cited by 34
Untitled work
Villania (2019) · doi:10.1016/j.ymssp.2018.07.028 · Cited by 34
Combining Snow and Earthquake Loads for Limit States Design
Ellingwood, B. R., & Rosowsky, D. V. (1996) · Journal of Structural Engineering · doi:10.1061/(asce)0733-9445(1996)122:11(1364 · Cited by 34
Assessing overdispersion and dose-response in the male dominant lethal assay
Lockhart, A.-M. C., Piegorsch, W. W., & Bishop, J. B. (1992) · Mutation Research/Environmental Mutagenesis and Related Subjects · doi:10.1016/0165-1161(92)90007-9 · Cited by 34
Neural network based stochastic design charts for settlement prediction
Shahin, M. A., Jaksa, M. B., & Maier, H. R. (2005) · Canadian Geotechnical Journal · doi:10.1139/t04-096 · Cited by 34
“Full details of the Monte Carlo technique are given by many authors (e.g., Hammersley and Handscomb 1964; Rubinstein 1981).”
Method
DNA histogram debris theory and compensation
Bagwell, C. B., Mayo, S. W., Whetstone, S. D., Hitchcox, S. A., Baker, D. R., Herbert, D. J., Weaver, D. L., Jones, M. A., & Lovett, E. J. (1991) · Cytometry · doi:10.1002/cyto.990120203 · Cited by 33
Predictive Assessment of Fish Health and Fish Kills in the Neuse River Estuary Using Elicited Expert Judgment
Borsuk, M. E. (2004) · Human and Ecological Risk Assessment An International Journal · doi:10.1080/10807030490438454 · Cited by 34
Review of Computational Fluid Dynamics in the Design of Floating Offshore Wind Turbines
Haider, R., Li, X., Shi, W., et al. (2024) · Energies · doi:10.3390/en17174269 · Cited by 33
“This consistency can be ensured through a strategic mix of boundary settings and the integration of novel source terms within the momentum equations [240–244].”
Background
A bi-level approximation tool for the computation of FRFs in stochastic dynamic systems
Chatterjee, T., Chakraborty, S., & Chowdhury, R. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.09.001 · Cited by 33
Bayesian assessment of an existing bridge: a case study
Jacinto, L., Neves, L. C., & Santos, L. O. (2015) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2014.995105 · Cited by 33
“This algorithm combines the Acceptance–Rejection and the composition methods (Rubinstein, 1981), and can be regarded as a direct algorithm.”
Method
Applying the Technology Choice Model in Consequential Life Cycle Assessment: A Case Study in the Peruvian Agricultural Sector
Larrea‐Gallegos, G., Vázquez‐Rowe, I., Fresco, H. C. W., & Kahhat, R. F. (2018) · Journal of Industrial Ecology · doi:10.1111/jiec.12812 · Cited by 34
“When the final step (i.e., pseudo-random number generation followed by optimization) is repeated a significant number of times, the statistics that summarize the simulations are a reasonable approximation of the properties of the uncertainty of the results (Rubinstein and Kroese 2016).”
Estimation of the proportion of population cannabis consumption in Australia that is accounted for by daily users using Monte Carlo Simulation
Chan, G., & Hall, W. (2020) · Addiction · doi:10.1111/add.14909 · Cited by 33
Confidence-Based Design Optimization for a More Conservative Optimum Under Surrogate Model Uncertainty Caused by Gaussian Process
Jung, Y., Kang, K., Cho, H., & Lee, I. (2021) · Journal of Mechanical Design · doi:10.1115/1.4049883 · Cited by 33
Radial and Uniform Distributions in Vector and Matrix Spaces for Probabilistic Robustness
Calafiore, G. C., Dabbene, F., & Tempo, R. (1999) · Springer eBooks · doi:10.1007/978-1-4471-0543-5_2 · Cited by 33
A spectral representation based model for Monte Carlo simulation
Grigoriu, M. D. (2000) · Probabilistic Engineering Mechanics · doi:10.1016/s0266-8920(99)00038-7 · Cited by 33
Automatic selection of parameters for vessel/neurite segmentation algorithms
Abdul‐Karim, M., Roysam, B., Dowell‐Mesfin, N., Jeromin, A., Yüksel, M., & Kalyanaraman, S. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852462 · Cited by 34
Stopping-Time Resampling for Sequential Monte Carlo Methods
Chen, Y., Xie, J., & Liu, J. S. (2005) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2005.00497.x · Cited by 33
“The method can be applied not only to standard Monte Carlo estimation problems but also to solving linear equations and integral equations ( Rubinstein (1981) , chapter 5). In fact, πθ.An/ in the coalescent model can be viewed as the solution to a system of linear equations (Sawyer et al., 1987; Lundstrom et al., 1992…”
Method
Novel algorithm using Active Metamodel Learning and Importance Sampling: Application to multiple failure regions of low probability
Razaaly, N., & Congedo, P. M. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.047 · Cited by 33
“Importance sampling (IS) techniques [2], [3] are part of this family, and have been developed over the past few decades to shift the underlying distribution towards the failure region so as to learn more efficiently information from rare events.”
Background
Reliability assessment of free spanning subsea pipeline
Shabani, M. M., Taheri, A., & Daghigh, M. (2017) · Thin-Walled Structures · doi:10.1016/j.tws.2017.08.026 · Cited by 33
HOLISTIC SEISMIC RISK ESTIMATION OF A METROPOLITAN CENTER
Cardona, O. D., & HURTADO, J. (2025) · World Conference of Earthquake Engineering · doi:10.71846/12-wcee-0672 · Cited by 33
Emergency control and its strategies
Wehenkel, L. A. (1999) · ORBi (University of Liège) · Cited by 33
Monte Carlo approaches to sampling forested tracts with lines or points
Valentine, H. T., Gove, J. H., & Grégoire, T. G. (2001) · Canadian Journal of Forest Research · doi:10.1139/x01-080 · Cited by 33
Adaptive hybrid conditional expectation approaches for reliability estimation
Karamchandani, A., & Cornell, C. A. (1991) · Structural Safety · doi:10.1016/0167-4730(91)90027-7 · Cited by 33
Decomposition of some weil-known variance reduction techniques
Nelson, B. L., & Schmeiser, B. W. (1986) · Journal of Statistical Computation and Simulation · doi:10.1080/00949658608810871 · Cited by 33
Monte Carlo Simulations in Nanomedicine: Advancing Cancer Imaging and Therapy
Chow, J. C. L. (2025) · Nanomaterials · doi:10.3390/nano15020117 · Cited by 32
Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design
Zhao, Q., Chen, X., Ma, Z.-D., et al. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/487686 · Cited by 32
“Such difficulties have motivated the development of various uncertainty propagation methods, such as MonteCarlo simulation (MCS) method [10], the first and second order reliability methods (FORM/SORM) [11], response surface method (RSM) [12], and the stochastic response surface method (SRSM) [13].”
Modification of the Audze–Eglājs criterion to achieve a uniform distribution of sampling points
Eliáš, J., & Vořechovský, M. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.07.004 · Cited by 32
Bayes estimation: A novel approach to derivation of internally consistent thermodynamic data for minerals, their uncertainties, and correlations. Part I: Theory
Olbricht, W., Chatterjee, N. D., & Miller, K. (1994) · Physics and Chemistry of Minerals · doi:10.1007/bf00205214 · Cited by 32
Mining data from hemodynamic simulations via Bayesian emulation
Kolachalama, V. B., Bressloff, N. W., & Nair, P. B. (2007) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-6-47 · Cited by 32
“A standard approach for statistical analysis is the Monte Carlo simulation technique [24], where the computer model is run repeatedly for randomly generated values of the inputs, and subsequently, the resulting data is postprocessed to estimate the output statistics.”
Method
Controlled release with coating layer of permeable particles
Ito, R., Golman, B., & Shinohara, K. (2003) · Journal of Controlled Release · doi:10.1016/s0168-3659(03)00363-8 · Cited by 32
Directional importance sampling for ill-proportioned spaces
Moarefzadeh, M. R., & Melchers, R. E. (1999) · Structural Safety · doi:10.1016/s0167-4730(98)00023-x · Cited by 32
Efficient, automated Monte Carlo methods for radiation transport
Kong, R., Ambrose, M., & Spanier, J. (2008) · Journal of Computational Physics · doi:10.1016/j.jcp.2008.06.037 · Cited by 32
“In this paper, we describe a new MC method, based on sequential application of correlated sampling [4,7], that achieves geometric convergence for very general transport problems.”
Method
Monte Carlo methods in statistical physics: Mathematical foundations and strategies
Kästner, M. (2009) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2009.06.011 · Cited by 32
“ix of a Markov chain such that the statistical errorpresent in a Monte Carlo estimator is minimal. An answer to the question of which transition matrix to choose is provided by the following theorem (Rubinstein, 1981). 9 Theorem 15 (Importance sampling). For a set of random variables {X n}N−1 n=0 distributed according…”
Background
Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models
Husmeier, D. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1127 · Cited by 32
“Sampling from the beta distribution is straightforward, as described, e.g. in Rubinstein (1981) .”
Method
Chance-Constrained Optimization-Based Parameter Estimation for Muskingum Models
Das, A. (2007) · Journal of Irrigation and Drainage Engineering · doi:10.1061/(asce)0733-9437(2007)133:5(487 · Cited by 32
Monte Carlo methods
Koch, K.-R. (2017) · GEM - International Journal on Geomathematics · doi:10.1007/s13137-017-0101-z · Cited by 32
Monte Carlo summation and integration applied to multiclass queuing networks
Ross, K. W., Tsang, D. H. K., & Wang, J. (1994) · Journal of the ACM · doi:10.1145/195613.195630 · Cited by 32
Unbiased monte carlo evaluation of certain functional integrals
Wagner, W. (1987) · Journal of Computational Physics · doi:10.1016/0021-9991(87)90017-9 · Cited by 32
Multiscale Chaotic SPSA and Smoothed Functional Algorithms for Simulation Optimization
Bhatnagar, S., & Borkar, V. S. (2003) · SIMULATION · doi:10.1177/0037549703039988 · Cited by 30
“Next we turn our attention to the smoothed functional scheme originally due to Katkovnik and Kulchitsky [13] (see also Rubinstein [14]).”
Method
Microphytobenthic potential productivity estimated in three tidal embayments of the San Francisco Bay: A comparative study
Guarini, J., Cloern, J. E., Edmunds, J. L., et al. (2002) · Estuaries · doi:10.1007/bf02695983 · Cited by 32
“Our purpose is to combine deterministic light availability modeling with a Monte-Carlo method (Rubinstein 1981) assessing the variability of microphytobenthos productivity estimates and then to compare the relative daily primary pro ductivity in three different intertidal habitats of San Francisco…”
Method
The winter simulation conference
Crain, R. C., Sussman, J. M., Schriber, T. J., et al. (1992) · Winter Simulation Conference · doi:10.1145/167293.167297 · Cited by 32
“During the WSC Renaissance Period, continuing developments in modeling languages and simulation methodology are reflected in such books as (ordered chronologically, and with the list intended to be suggestive, not exhaustive): Computer Modeling and Simulation (Martin 1968); Simulation with GPSS and GPSS V (Bobillie…”
BackgroundMethodInfluential
Improving Hedonic Estimation with an Inequality Restricted Estimator
Gilley, O. W., & Pace, R. K. (1995) · The Review of Economics and Statistics · doi:10.2307/2109810 · Cited by 32
Predictive modelling and validation of Pseudomonas fluorescens growth at superatmospheric oxygen and carbon dioxide concentrations
Geysen, S., Geeraerd, A. H., VERLINDEN, B. E., et al. (2004) · Food Microbiology · doi:10.1016/j.fm.2004.08.007 · Cited by 32
Allocation of Geometric Tolerances: New Criterion and Methodology
Nassef, A. O., & ElMaraghy, H. A. (1997) · CIRP Annals · doi:10.1016/s0007-8506(07)60785-9 · Cited by 32
Assessment of Probability Distribution of Dissolved Oxygen Deficit
Tung, Y., & Hathhorn, W. E. (1988) · Journal of Environmental Engineering · doi:10.1061/(asce)0733-9372(1988)114:6(1421 · Cited by 32
Analysis of PMD compensators with fixed DGD using importance sampling
Lima, I. T., Biondini, G., Marks, B. S., et al. (2002) · IEEE Photonics Technology Letters · doi:10.1109/68.998706 · Cited by 32
“The procedure for patching different values of together is described in [ 8 ]‐[10].”
Method
Metaheuristics in Stochastic Combinatorial Optimization: a Survey
Bianchi, L., Dorigo, M., & Gambardella, L. M. (2006) · SUPSI ARIS · doi:10.71910/supsi.6876 · Cited by 32
PDEM-based multi-component and global reliability evaluation framework for steel tubular transmission towers with semi-rigid connections
Tang, Z., Li, Z., Wang, T., et al. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116838 · Cited by 31
Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments
Feng, C., Valdebenito, M. A., Chwała, M., et al. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.09.006 · Cited by 31
A novel modified artificial rabbit optimization for stochastic energy management of a grid-connected microgrid: A case study in China
Khan, N. H., Wang, Y., Jamal, R., Iqbal, S., Elbarbary, Z. M. S., Alshammari, N. F., Ebeed, M., & Jurado, F. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.05.018 · Cited by 31
An active learning method based on Monte Carlo dropout neural network for high-dimensional reliability analysis
Sun, H., & Bao, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111169 · Cited by 33
Fragility analysis of bridges due to overweight traffic load
Fiorillo, G., & Ghosn, M. (2017) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2017.1380675 · Cited by 31
“Variations of the MCS technique are often employed when analyzing the reliability of structural systems (Rubinstein, 1981; Hurtado & Barbat, 1998; Naess et al., 2009).”
Method

Authors: Rubinstein, R. Y., & Kroese, D. P.
ORCID: Kroese, Dirk P.
Venue: Wiley Series in Probability and Statistics
Publisher: Wiley
Published: 2016-11-21
ISSN: 1940-6347 (ISSN-L)
DOI: 10.1002/9781118631980
OpenAlex: W2147632348
Cited by: 3,356
References: 0
Citations by year: 2026: 131 · 2025: 182 · 2024: 168 · 2023: 157 · 2022: 167 · 2021: 162 · 2020: 171 · 2019: 139 · 2018: 118 · 2017: 66 · 2016: 41
FWCI: 52.82
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley series in probability and statistics (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
International Linear Collider (submittedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Mathematics › Numerical Analysis › Mathematical Approximation and Integration
Keywords: Monte Carlo simulation, stochastic simulation, random number generation, probability distributions, variance reduction, Markov chain Monte Carlo

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Simulation and the Monte Carlo Method

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks