The DOI matches its Crossref record.
Simulation and the Monte Carlo Method
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Convergence to low variability in terms of mean and standard deviations is reached at M=103, where both metrics stabilized with minimal variation beyond that point and correspond to the plateau of the coefficient of variation [40].”
Citations
Cited by
Sorted by citations
Items 501–600 of 1,003
Guilleminot, J., Soize, C., Kondo, D., et al. (2008) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2008.06.002 · Cited by 52
“(24) can be solved by Monte-Carlo numerical simulations [13] which require the computation of the macroscopic moduli for each realization.”
BackgroundFragility based damage assesment in existing precast industrial buildings: A case study for Turkey
Şenel, Ş. M., & Kayhan, A. H. (2010) · STRUCTURAL ENGINEERING AND MECHANICS · doi:10.12989/sem.2010.34.1.039 · Cited by 52
Estimation and propagation of parametric uncertainty in environmental models
McIntyre, N. R., Wheater, H. S., & Lees, M. J. (2002) · Journal of Hydroinformatics · doi:10.2166/hydro.2002.0018 · Cited by 52
Shayanfar, M. A., Barkhordari, M. A., & Roudak, M. A. (2016) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2016.11.021 · Cited by 52
A comparative study of two interval-random models for hybrid uncertainty propagation analysis
Wang, C., & Matthies, H. G. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106531 · Cited by 52
Quantifying Uncertainty in Critical Loads: (A) Literature Review
Skeffington, R. A. (2005) · Water Air & Soil Pollution · doi:10.1007/s11270-006-0382-6 · Cited by 52
Approximate counting by sampling the backtrack-free search space
Gogate, V., & Dechter, R. (2007) · Cited by 52
Yang, J., Tao, J., Sudret, B., et al. (2019) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.6277 · Cited by 51
“pZΘ(z, θ, t)|t=t0 = δ(z − z0)pΘ(θ), (9)”
Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
Sinibaldi, A., Giuliani, C., Carleo, G., & Vicentini, F. (2023) · Quantum · doi:10.22331/q-2023-10-10-1131 · Cited by 51
“To recover this behaviour in the case of the infi-delity optimization we propose a new estimator based upon the Control Variates (CV) technique [64]: where c ∈ R .”
BackgroundMethodStructural integrity assessment of offshore tubular joints based on reliability analysis
Rajasankar, J. (2003) · International Journal of Fatigue · doi:10.1016/s0142-1123(03)00021-5 · Cited by 51
Multiobjective Robust Power System Expansion Planning Considering Generation Units Retirement
Mavalizadeh, H., Ahmadi, A., Gandoman, F. H., et al. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2672694 · Cited by 51
Stochastic multiscale models for fracture analysis of functionally graded materials
Chakraborty, A., & Rahman, S. (2007) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2007.10.013 · Cited by 51
Misspecification and the pricing and hedging of long-term foreign currency options
Melino, A., & Turnbull, S. M. (1995) · Journal of International Money and Finance · doi:10.1016/0261-5606(95)00003-w · Cited by 51
An Exponential-Family Multidimensional Scaling Mixture Methodology
Wedel, M., & DeSarbo, W. S. (1996) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1996.10524674 · Cited by 51
Advertising Rates, Audience Composition, and Competition in the Network Television IndustryPreprint
Goettler, R. L. (1999) · RePEc: Research Papers in Economics · Cited by 51
Reliability of material and geometrically non-linear reinforced and prestressed concrete structures
Biondini, F., Bontempi, F., Frangopol, D. M., et al. (2004) · Computers & Structures · doi:10.1016/j.compstruc.2004.03.010 · Cited by 50
“A numerical approach is then required and the reliability analysis can be performed by Monte Carlo simulation [ 6 ], where repeated analyses are carried out with random outcomes of the basic variables X generated in accordance to their marginal density functions fXi ðxiÞ, i ¼ 1; ... ; n. Based on the sample obtained t…”
MethodThreshold Regression With a Threshold Boundary
Yu, P., & Fan, X. (2020) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1740712 · Cited by 51
Design of full-strength full-ductility extended end-plate beam-to-column joints
Francavilla, A. B., Latour, M., Piluso, V., et al. (2018) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2018.05.013 · Cited by 50
Wang, T., Teng, F., Deng, X., & Xie, J. (2022) · One Earth · doi:10.1016/j.oneear.2022.06.005 · Cited by 49
Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers
Asl, M. B., Fofana, I., & Meghnefi, F. (2024) · Energies · doi:10.3390/en17143533 · Cited by 50
A Review of the Weibull Distribution and Selected Engineering Applications
Luko, S. N. (1999) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/1999-01-2859 · Cited by 50
Uncertainty analysis of complex structural systems
Schuëller, G. I., & Pradlwarter, H. J. (2009) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.2549 · Cited by 50
“The second main application of Monte Carlo procedures is the evaluation of higher-dimensional integrals [22, 23] needed for reliability analysis and for the identification of the failure domains by random search algorithms [30].”
MethodRajabi, M. M., & Ataie‐Ashtiani, B. (2014) · Advances in Water Resources · doi:10.1016/j.advwatres.2014.02.004 · Cited by 50
Adaptive Blind Deconvolution of Linear Channels Using Renyi's Entropy with Parzen Window Estimation
Erdoğmuş, D., Hild, K. E., Príncipe, J. C., et al. (2004) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.827202 · Cited by 50
“entropy estimator [32] (which disclosed links with convolution smoothing [43]), we suggest using a reasonably large kernel size to produce a cost surface with fewer local minima.”
MethodDynamic risk assessment in healthcare based on Bayesian approach
Li, M., Liu, Z., Li, X., et al. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.04.040 · Cited by 50
Use of EV hosting capacity for management of low‐voltage distribution systems
Barbosa, T., Andrade, J. C. G., Torquato, R. J. S., Freitas, W., & Trindade, F. C. L. (2020) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2019.1791 · Cited by 50
“To consider stochastic variables, a MC-based analysis is applied [18] in a wide-scale study to estimate the EV hosting capacity of 75,550 real LV systems from a distribution utility, monitoring voltage quality (magnitude and unbalance) and asset (line and transformer) utilisation.”
MethodImportance sampling for randomly excited dynamical systems
Macke, M., & Bucher, C. G. (2003) · Journal of Sound and Vibration · doi:10.1016/s0022-460x(03)00204-9 · Cited by 50
Möller, B., Beer, M., Graf, W., et al. (2006) · Computers & Structures · doi:10.1016/j.compstruc.2005.10.006 · Cited by 50
A perspective on variance reduction in dynamic simulation experiments
Nelson, B. L. (1987) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918708812597 · Cited by 50
Variable resolution particle filter
Verma, V., Thrun, S., & Simmons, R. G. (2003) · Cited by 51
Transport of phosphate in a heterogeneous field
van der Zee, S. E. A. T. M., & van Riemsdijk, W. H. (1986) · Transport in Porous Media · doi:10.1007/bf00208042 · Cited by 50
“The distributions were tested using the Kolmogorov-Smirnov test statistic ( Rubinstein, 1981 ), given by the maximal value of IPr-f'rl in the sample.”
MethodMeimaroglou, D., Pladis, P., Baltsas, A., et al. (2011) · Chemical Engineering Science · doi:10.1016/j.ces.2011.01.003 · Cited by 49
“, Px,z and Dr x,m z) are assumed to be uniformly distributed in the range [0,m) and [m,0), respectively, following the outcome of a random (pseudo-random) number generator in the range [0,1) (Rubinstein, 1981).”
BackgroundMultiobjective global surrogate modeling, dealing with the 5-percent problem
Gorissen, D., Couckuyt, I., Laermans, E., et al. (2009) · Engineering With Computers · doi:10.1007/s00366-009-0138-1 · Cited by 49
“Sample data were obtained by applying an acceptation–rejection method [81].”
MethodPellissetti, M. F., Capiez‐Lernout, E., Pradlwarter, H. J., et al. (2008) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.08.004 · Cited by 49
Random Uncertainties Model in Dynamic Substructuring Using a Nonparametric Probabilistic Model
Soize, C., & Chebli, H. (2003) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2003)129:4(449 · Cited by 49
Shah, M., Vaidya, D., & SIRCAR, A. (2018) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-018-0008-x · Cited by 49
“6) (Rubinstein 1981).”
BackgroundInfluentialUrgun, D., Singh, C., & Vittal, V. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2944131 · Cited by 49
“Detailed technical information can be obtained from [31].”
BackgroundA two Timescale Stochastic Approximation Scheme for Simulation-Based Parametric Optimization
Bhatnagar, S., & Borkar, V. S. (1998) · Probability in the Engineering and Informational Sciences · doi:10.1017/s0269964800005362 · Cited by 44
“Section 7.6 of [10] also describes the technique of smoothed functions, which avoids direct approximation of the gradient.”
MethodInfluentialMultiphase flow in deforming porous material
Schrefler, B. A. (2004) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.953 · Cited by 49
Global optimization by random perturbation of the gradient method with a fixed parameter
Pogu, M., & de Cursi, E. S. (1994) · Journal of Global Optimization · doi:10.1007/bf01100691 · Cited by 49
“Several modifications of the basic procedure (1) have been considered as multistart, statistical gradient or trajectory methods (see, for instance [ 10 ], [12]).”
MethodInfluentialGeneration of correlated parameters for statistical circuit simulation
Eshbaugh, K. (1992) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/43.170985 · Cited by 49
Probabilistic models with unknown objects
Russell, S., & Milch, B. (2006) · Cited by 49
Approximate probability propagation with mixtures of truncated exponentials
Rumí, R., & Salmerón, A. (2006) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2006.06.007 · Cited by 48
“The efficiency [24] of this method is ef 1⁄4 1= P i 62N pi , which means that each value x* generated has a probability ef of being accepted as a value for X.”
MethodInfluentialOptimal operation of a droop‐controlled DCMG with generation and load uncertainties
Maulik, A., & Das, D. (2018) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2017.1443 · Cited by 48
Modelling Cooling of Packaged Fruit Using 3D Shape Models
Gruyters, W., Verboven, P., Diels, E., et al. (2018) · Food and Bioprocess Technology · doi:10.1007/s11947-018-2163-9 · Cited by 48
“On this dataset, the covariance decomposition algorithm (Rubinstein, 1981) used the average of each descriptor cn, m, ak, bk, and the covariance between the descriptors to randomly create new sets of shape descriptors.”
MethodVicenzutti, A., Menis, R., & Sulligoi, G. (2019) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2019.2920334 · Cited by 48
Uncertainties in Determination of pH
Meinrath, G., & Spitzer, P. (2000) · Microchimica Acta · doi:10.1007/s006040070005 · Cited by 48
“From these confidence regions, 1000 normally distributed random variates are drawn by Monte Carlo procedures [ 28 ] to illustrate that comparison of different statistical procedures have to be based on the confidence ellipses.”
MethodGradient and parameter sensitivity estimation for systems evaluated using Monte Carlo analysis
Ahammed, M., & Melchers, R. E. (2005) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2005.04.005 · Cited by 48
“It is well-known that it is advisable to use the same set of (pseudo-) random numbers for both analyses thereby ensuring that sampling and numerical errors do not swamp the result being sought [2].”
Software dependability evaluation based on Markov usage models
Gutjahr, W. J. (2000) · Performance Evaluation · doi:10.1016/s0166-5316(99)00052-8 · Cited by 48
Ihme, M., Marsden, A. L., & Pitsch, H. G. (2007) · Neural Computation · doi:10.1162/neco.2007.08-06-316 · Cited by 48
“Sample data are obtained by applying an acception-rejection method (Rubinstein, 1981) that results in higher sampling density in regions where the functional value Y is large.”
MethodParallel simulation of stochastic Petri nets using recurrence equations
Baccelli, F., & Canales, M. (1993) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/151527.151545 · Cited by 48
Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers
Yeşilyurt, S., & Patera, A. T. (1995) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/0045-7825(94)00684-f · Cited by 47
Experiments in concurrent stochastic simulation: The EcliPSe paradigm
Rego, V. J., & Sunderam, V. S. (1992) · Journal of Parallel and Distributed Computing · doi:10.1016/0743-7315(92)90098-8 · Cited by 48
“• Monte Carlo solutions to matrix problems [44].”
BackgroundBianchi, L., & Dorigo, M. (2006) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 48
Robust Simulation-Based Estimation of ARMA Models
de Luna, X., & Genton, M. G. (2001) · Journal of Computational and Graphical Statistics · doi:10.1198/10618600152628347 · Cited by 47
“Pseudo-random numbers for Step 2 were obtainedvia the algorithm of linear congruences; see Rubinstein (1981).”
MethodSobieraj, J., & Metelski, D. (2022) · Applied Sciences · doi:10.3390/app12031044 · Cited by 68
Structural reliability analysis by line sampling: A Bayesian active learning treatment
Dang, C., Valdebenito, M. A., Faes, M. G. R., et al. (2023) · Structural Safety · doi:10.1016/j.strusafe.2023.102351 · Cited by 47
“A non-exhaustive list of such techniques includes Monte Carlo simulation (MCS) [2], importance sampling [3, 4], directional sampling [5, 6], subset simulation [7, 8] and line sampling (LS) [9, 10].”
Ma, Z., Luan, Y., Zhang, F., et al. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109963 · Cited by 47
Towards flexible management of postharvest variation in fruit firmness of three apple cultivars
Gwanpua, S. G., VERLINDEN, B. E., Hertog, M., et al. (2013) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2013.04.015 · Cited by 47
Driot, N., & Perret-Liaudet, J. (2005) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2005.09.031 · Cited by 47
Large-Scale Network Parameter Configuration Using an On-Line Simulation Framework
Ye, T., Kaur, H. T., Kalyanaraman, S., & Yüksel, M. (2008) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.2001729 · Cited by 45
“[33] R.”
BackgroundProbabilistic sizing of battery energy storage when time-of-use pricing is applied
Guido Carpinelli, M. S., Mottola, F., & Proto, D. (2016) · Electric Power Systems Research · doi:10.1016/j.epsr.2016.07.013 · Cited by 47
Energy damage index based on capacity and response spectra
Alvarado, S. A. D., Pujades, L. G., Barbat, H. A. B., et al. (2017) · Engineering Structures · doi:10.1016/j.engstruct.2017.09.019 · Cited by 47
Shahmohammadi, S., Steinmann, Z. J. N., Clavreul, J., Hendrickx, H., King, H., & Huijbregts, M. A. J. (2017) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-017-1426-4 · Cited by 48
“A Monte Carlo simulation (Rubinstein and Kroese 2016) was used to quantify the variability in GHG footprints of laundry washing due to variability in (i) detergent format, (ii) detergent dosage (actual), (iii) GHG emissions related to detergent production, (iv) wash temperature, (v) machine energy use within a range o…”
MethodActive learning and active subspace enhancement for PDEM-based high-dimensional reliability analysis
Zhou, T., & Peng, Y. (2020) · Structural Safety · doi:10.1016/j.strusafe.2020.102026 · Cited by 47
On the bias and variance in tree volume predictions due to model and measurement errors
Kangas, A. S. (1996) · Scandinavian Journal of Forest Research · doi:10.1080/02827589609382937 · Cited by 47
Cramer, J., Fiori, S., M�ller, G., et al. (1999) · Journal of Molecular Modeling · doi:10.1007/s0089490050287 · Cited by 47
Reliability‐Based Design of Intergreen Interval at Traffic Signals
Easa, S. M. (1993) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(1993)119:2(255 · Cited by 47
Latin Hypercube Sampling Monte Carlo Estimation of Average Quality Index for Integrated Circuits
Keramat, M., & Kielbasa, R. (1997) · Analog Integrated Circuits and Signal Processing · doi:10.1023/a:1008207113480 · Cited by 47
“Kluwar Academic Publisher, 1995.”
MethodInfluentialComputer-aided modeling, analysis, and design of communication networks
Kurose, J., & Mouftah, H. T. (1988) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.192737 · Cited by 47
Reinforcement learning by policy search
Kaelbling, L. P., & Peshkin, L. (2002) · Cited by 47
Seydel, R. U. (2012) · Universitext · doi:10.1007/978-1-4471-2993-6_5 · Cited by 46
Comerford, L., Kougioumtzoglou, I. A., & Beer, M. (2014) · Structural Safety · doi:10.1016/j.strusafe.2014.10.001 · Cited by 46
“For instance, in a Monte Carlo analysis it is often required that millions, or even billions of samples are considered to estimate rare events probabilities reliably [23].”
BackgroundA new adaptive importance sampling Monte Carlo method for structural reliability
Jahani, E., Shayanfar, M. A., & Barkhordari, M. A. (2013) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-013-1779-6 · Cited by 46
“And finally the failure probability is calculated by dividing the times in which the limit state function is negative to total sampling numbers (Melchers, 1989; Rubinstein, 1981; Zou et al., 2002) Each of these mentioned methods has some drawbacks, some of which are presented in the rest of the paper.”
BackgroundApplication of the control variate technique to estimation of total sensitivity indices
Kucherenko, S. S., Delpuech, B., Iooss, B., et al. (2014) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2014.07.008 · Cited by 46
“There are various variance reduction techniques used to increase the accuracy of the MC estimates [14].”
MethodA new maximum entropy-based importance sampling for reliability analysis
Dai, H., Zhang, H., & Wang, W. (2016) · Structural Safety · doi:10.1016/j.strusafe.2016.08.001 · Cited by 46
High-performance, color-tunable fiber shaped organic light-emitting diodes
Ko, K., Lee, H. B., Kim, H. M., Lee, G. J., Shin, S.-R., Kumar, N., Song, Y. M., & Kang, J. (2018) · Nanoscale · doi:10.1039/c8nr05120h · Cited by 46
Chakraborty, S., & Tesfamariam, S. (2021) · Structural Safety · doi:10.1016/j.strusafe.2020.102073 · Cited by 46
Reliability analysis of spacecraft structures under static and dynamic loading
Pellissetti, M. F., Schuëller, G. I., Pradlwarter, H. J., et al. (2006) · Computers & Structures · doi:10.1016/j.compstruc.2006.03.009 · Cited by 46
Computationally Efficient Nonparametric Importance Sampling
Neddermeyer, J. C. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.0122 · Cited by 46
“Under the assumptions that Iu and varq[uw] are finite, a CLT guaranties ffiffiffiffi N p ðÎ IS u IuÞ0Nð0;s(2)ISÞ; where s 2 IS 1⁄4 Eq[uw Iu](2) (Rubinstein 1981).”
Ketelaere, B. D., Lammertyn, J., Molenberghs, G., et al. (2004) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2004.03.007 · Cited by 46
“Monte-Carlo simulations (n = 1000) (Rubinstein, 1981) based on this distribution were performed resulting in 1000 tomato firmness profiles of tomatoes versus time for each cultivar, for both harvests.”
MethodOptimal Calibration of Parallel Kinematic Machines
Verner, M., Xi, F., & Mechefske, C. (2005) · Journal of Mechanical Design · doi:10.1115/1.1828461 · Cited by 46
A Random Number Generator Based on Quantum Entangled Photon Pairs
Hai-Qiang, M., Su-Mei, W., Da, Z., et al. (2004) · Chinese Physics Letters · doi:10.1088/0256-307x/21/10/027 · Cited by 46
Modelling of phenomena within catalyst particles
Keil, F. J. (1996) · Chemical Engineering Science · doi:10.1016/0009-2509(96)00020-6 · Cited by 46
Sensitivity analysis and the “what if” problem in simulation analysis
Arsham, H., Feuerverger, A., McLeish, D. L., et al. (1989) · Mathematical and Computer Modelling · doi:10.1016/0895-7177(89)90434-2 · Cited by 46
“According to the acceptance-rejection method [ 33 ] one presents the pdff(y) as”
MethodSelf-citationRigid multibody system dynamics with uncertain rigid bodies
Batou, A., & Soize, C. (2011) · Multibody System Dynamics · doi:10.1007/s11044-011-9279-2 · Cited by 45
“(49) The random mass matrix [ M ] is defined by in which the ( 3 n b × 3 n b ) random matrices [ M r ] and [ M ] s are defined by Random (46) and (47) are solved using the Monte Carlo simulation method [18].”
MethodOn solving integral equations using Markov chain Monte Carlo methods
Doucet, A., Johansen, A. M., & Tadić, V. B. (2010) · Applied Mathematics and Computation · doi:10.1016/j.amc.2010.03.138 · Cited by 45
“Section 2.1.1 presents the algorithm most commonly presented in the literature; Section 2.1.2 sketches some techniques for reducing the variance of the estimator provided by this algorithm and a path-space interpretation illustrating the unbiasedness of these techniques is given in Section 2.1.3.”
BackgroundMethodInfluentialDiscussing the “big n problem”
Lasinio, G. J., Mastrantonio, G., & Pollice, A. (2012) · Statistical Methods & Applications · doi:10.1007/s10260-012-0207-2 · Cited by 45
“Some recent proposals aiming at minimizing the limitations of the INLA implementation are given in Ji et al. (2011) where the IS-LA, MH-LA an MLA approaches are introduced, that combine Importance Sampling (Rubinstein 1981), Metropolis Hastings and a Marginalization based approach with the Laplace approximation respec…”
MethodRoughness and Discharge Uncertainty in 1D Water Level Calculations
Bozzi, S., Passoni, G., Bernardara, P., et al. (2014) · Environmental Modeling & Assessment · doi:10.1007/s10666-014-9430-6 · Cited by 45
Development of Artificial Neural Networks for Prediction of Compression Coefficient of Soft Soil
Pham, B. T., Nguyen, M. D., Ly, H., et al. (2019) · Lecture notes in civil engineering · doi:10.1007/978-981-15-0802-8_187 · Cited by 45
Billinton, R., & Jonnavithula, A. (1997) · IEE Proceedings - Generation Transmission and Distribution · doi:10.1049/ip-gtd:19970929 · Cited by 45
Probability analysis method using Fast Fourier transform and its application
Sakamoto, J., Mori, Y., & Sekioka, T. (1997) · Structural Safety · doi:10.1016/s0167-4730(96)00032-x · Cited by 45
Importance sampling for first passage problems of nonlinear structures
Bayer, V., & Bucher, C. G. (1999) · Probabilistic Engineering Mechanics · doi:10.1016/s0266-8920(98)00014-9 · Cited by 45
Maiti, S., Gupta, G., Erram, V. C., & Tiwari, R. K. (2011) · Nonlinear processes in geophysics · doi:10.5194/npg-18-179-2011 · Cited by 45
“, genetic algorithm and simulated annealing techniques have proven to be useful whilst a good staring model is not available (Rubinstein, 1981; Kirkpatrick et al., 1983; Horne and MacBeth, 1994) for offering global solution.”
BackgroundIssues related to structural aging in probabilistic risk assessment of nuclear power plants
Ellingwood, B. R. (1998) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(98)00018-0 · Cited by 45
Approximate reliability analysis using normal polynomial and simulation results
HONG, H. P., & Lind, N. C. (1996) · Structural Safety · doi:10.1016/s0167-4730(96)00018-5 · Cited by 45
Adjoint equations and random walks for illumination computation
Pattanaik, S. N., & Mudur, S. P. (1995) · ACM Transactions on Graphics · doi:10.1145/200972.200985 · Cited by 45
Linear momentum transfer investigated in the 20Ne + 197Au and 209Bi systems at 30 MeV/u
Rana, G. L., Nebbia, G., Tomasi, E., et al. (1983) · Nuclear Physics A · doi:10.1016/0375-9474(83)90317-2 · Cited by 45
Uncertainty in Regional‐Scale Assessments of Non‐Point Source Pollutants
Loague, K., & Corwin, D. L. (1996) · SSSA special publication series · doi:10.2136/sssaspecpub48.c7 · Cited by 45
“Monte Carlo methods involve the repeated sampling of the probability distribution for model parameters, variables, boundary conditions, and initial conditions, and the use of each set of samples in a simulatio n ( Rubenstein, 1981 ).”
MethodKay, S. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2046437 · Cited by 44
“Some of these issues addressed from a theoretical viewpoint can be found in [20].”
Background
Metadata
- 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)
- 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
- 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
