Relevant bibliographies by topics / Probability Sampling Techniques

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Probability Sampling Techniques'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Probability Sampling Techniques"

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Stratton, Samuel J. "Population Sampling: Probability and Non-Probability Techniques." Prehospital and Disaster Medicine 38, no. 2 (2023): 147–48. http://dx.doi.org/10.1017/s1049023x23000304.

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Wang, Xinyuan. "Use of proper sampling techniques to research studies." Applied and Computational Engineering 57, no. 1 (2024): 141–45. http://dx.doi.org/10.54254/2755-2721/57/20241324.

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This article extensively examines several sampling strategies, emphasising probability and non-probability sampling approaches. Sampling is essential in research as it influences the degree to which research findings accurately represent and can be applied to a larger population. This article provides a definition of probability sampling and explores its different approaches. The text highlights the benefits of probability sampling, such as its capacity to guarantee impartial selection and facilitate statistical inference. Furthermore, this article delves into the constraints and factors to be
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Berndt, Andrea E. "Sampling Methods." Journal of Human Lactation 36, no. 2 (2020): 224–26. http://dx.doi.org/10.1177/0890334420906850.

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Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration. In addition, issues related to sampling methods are described to highlight potential problems.
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Kim, Jae Kwang, and Zhonglei Wang. "Sampling Techniques for Big Data Analysis." International Statistical Review 87, S1 (2018): S177—S191. http://dx.doi.org/10.1111/insr.12290.

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Weerahandi, Samaradasa, and Robert G. White. "Some survey techniques for sampling rare subjects." Statistics & Probability Letters 15, no. 5 (1992): 361–68. http://dx.doi.org/10.1016/0167-7152(92)90154-w.

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Haslett, Stephen. "Sampling Techniques for Forest Inventories by Daniel Mandallaz." International Statistical Review 76, no. 2 (2008): 326–27. http://dx.doi.org/10.1111/j.1751-5823.2008.00054_27.x.

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Murtiarso, I. Bagus Hernu, Yuliana Isma Graha, and Niko Alnabawi. "Analysis of the Role of Quality on Job Satisfaction in Management with Sampling Techniques." Aptisi Transactions on Management (ATM) 2, no. 1 (2018): 28–36. http://dx.doi.org/10.33050/atm.v2i1.785.

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Basically in each management at the College has rules and procedures for writing a study, including in using the research population and sampling techniques to ensure its accuracy. In this study discusses the notion of population and the types of population in a study, sampling units and framework for sampling in Educational research, sample concepts along with the types of sampling, and complete reasons for using sampling and conducting census research. , various sampling methods that will be described as both probability sampling and non probability sampling, as well as explaining the reason
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Williamson, Derek G., Maosheng Yao, and John McFadden. "Monte Carlo Simulation in Sampling Techniques of Traffic Data Collection." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (2002): 91–97. http://dx.doi.org/10.3141/1804-13.

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Traffic volume counts are used in transportation planning, design, operation, and safety analyses. A new methodology establishes a statistical basis for comparing traffic volumes generated from different samples. Monte Carlo simulation was used to generate a cumulative probability function (CPF) of traffic volumes based on the fit-of-Weibull probability distribution to a particular traffic sample. A 90% confidence interval of the traffic volumes from a given traffic sample was obtained from the CPF and was used to compare different traffic samples. A case study was performed by using this meth
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Marta dinata, Riadi. "Optimasi Sampling Bersama Roulette-Wheel Sebagai Improve Metode Sample Random Sampling." Jurnal Komputasi 12, no. 2 (2024): 176–78. https://doi.org/10.23960/komputasi.v12i2.268.

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The Sample Random Sampling (SRS) method, which is commonly used in sampling, can improve theaccuracy of the results by carrying out leveling techniques based on probability values. Population data wasobtained from the 2019 Indonesian President & Vice Presidential Election Vote Count Results website forLampung Province. The research began with techniques for determining the number of respondents (voters), toa list of polling places (TPS) in which areas should be occupied by quick-count volunteers. The addition of"Roullet-Whell" computing to SRS (ImproveSRS) produces be
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Kertész, Gábor. "Deep Metric Learning Using Negative Sampling Probability Annealing." Sensors 22, no. 19 (2022): 7579. http://dx.doi.org/10.3390/s22197579.

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Multiple studies have concluded that the selection of input samples is key for deep metric learning. For triplet networks, the selection of the anchor, positive, and negative pairs is referred to as triplet mining. The selection of the negatives is considered the be the most complicated task, due to a large number of possibilities. The goal is to select a negative that results in a positive triplet loss; however, there are multiple approaches for this—semi-hard negative mining or hardest mining are well-known in addition to random selection. Since its introduction, semi-hard mining was proven
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Dissertations / Theses on the topic "Probability Sampling Techniques"

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Good, Norman Markus. "Methods for estimating the component biomass of a single tree and a stand of trees using variable probability sampling techniques." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37097/1/37097_Good_2001.pdf.

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This thesis developed multistage sampling methods for estimating the aggregate biomass of selected tree components, such as leaves, branches, trunk and total, in woodlands in central and western Queensland. To estimate the component biomass of a single tree randomised branch sampling (RBS) and importance sampling (IS) were trialed. RBS and IS were found to reduce the amount of time and effort to sample tree components in comparison with other standard destructive sampling methods such as ratio sampling, especially when sampling small components such as leaves and small twigs. However, RBS did
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Järnberg, Emelie. "Dynamic Credit Models : An analysis using Monte Carlo methods and variance reduction techniques." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197322.

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In this thesis, the credit worthiness of a company is modelled using a stochastic process. Two credit models are considered; Merton's model, which models the value of a firm's assets using geometric Brownian motion, and the distance to default model, which is driven by a two factor jump diffusion process. The probability of default and the default time are simulated using Monte Carlo and the number of scenarios needed to obtain convergence in the simulations is investigated. The simulations are performed using the probability matrix method (PMM), which means that a transition probability matri
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Subramanian, Harshavardhan. "Combining scientific computing and machine learning techniques to model longitudinal outcomes in clinical trials." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176427.

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Scientific machine learning (SciML) is a new branch of AI research at the edge of scientific computing (Sci) and machine learning (ML). It deals with efficient amalgamation of data-driven algorithms along with scientific computing to discover the dynamics of the time-evolving process. The output of such algorithms is represented in the form of a governing equation(s) (e.g., ordinary differential equation(s), ODE(s)), which one can solve then for any time point and, thus, obtain a rigorous prediction.  In this thesis, we present a methodology on how to incorporate the SciML approach in the cont
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Hee, Sonke. "Computational Bayesian techniques applied to cosmology." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273346.

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This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low st
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Noorizadeh, Emad. "Highly degenerate diffusions for sampling molecular systems." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7584.

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This work is concerned with sampling and computation of rare events in molecular systems. In particular, we present new methods for sampling the canonical ensemble corresponding to the Boltzmann-Gibbs probability measure. We combine an equation for controlling the kinetic energy of the system with a random noise to derive a highly degenerate diffusion (i.e. a diffusion equation where diffusion happens only along one or few degrees of freedom of the system). Next the concept of hypoellipticity is used to show that the corresponding Fokker-Planck equation of the highly degenerate diffusion is we
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Ben, Issaid Chaouki. "Effcient Monte Carlo Simulations for the Estimation of Rare Events Probabilities in Wireless Communication Systems." Diss., 2019. http://hdl.handle.net/10754/660001.

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Simulation methods are used when closed-form solutions do not exist. An interesting simulation method that has been widely used in many scientific fields is the Monte Carlo method. Not only it is a simple technique that enables to estimate the quantity of interest, but it can also provide relevant information about the value to be estimated through its confidence interval. However, the use of classical Monte Carlo method is not a reasonable choice when dealing with rare event probabilities. In fact, very small probabilities require a huge number of simulation runs, and thus, the computatio
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Chien, Truong Khac, and 張克戰. "Predicting default probability in construction industry basing on Over-Sampling Technique forGrey SystemTheory." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/20538532406710134290.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>102<br>Bankruptcy Prediction has been a hotly-debated topic among many people in business area. The fact is that once the firm goes bankrupt, it will be disastrous to not only firm itself but also other stakeholders. Many available methods have been applied to predict the possibility of business collapse; almost all of them were based on financial ratio analysis. Grey System Theory, used in the previous thesis for predicting default probability of construction firms, has brought some feasible results, by relying on the 19 initial financial ratios. This study, with
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Books on the topic "Probability Sampling Techniques"

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Network and Adaptive Sampling Techniques. Taylor & Francis Group, 2014.

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Randomized response and indirect questioning techniques in surveys. Chapman and Hall/CRC, 2010.

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Mukerjee, Rahul, and Arijit Chaudhuri. Randomized Response: Theory and Techniques. CRC Press LLC, 2020.

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Mukerjee, Rahul, and Arijit Chaudhuri. Randomized Response: Theory and Techniques. CRC Press LLC, 2020.

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Randomized Response: Theory and Techniques. CRC Press LLC, 2020.

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Randomized Response: Theory and Techniques. CRC Press LLC, 2020.

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Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

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Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and the
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Book chapters on the topic "Probability Sampling Techniques"

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Aboushelbaya, Ramy, Taimir Aguacil, Qiuting Huang, and Peter A. Norreys. "Efficient Location-Based Tracking for IoT Devices Using Compressive Sensing and Machine Learning Techniques." In High-Dimensional Optimization and Probability. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00832-0_12.

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AbstractIn this chapter, a scheme based on compressive sensing (CS) for the sparse reconstruction of down-sampled location data is presented for the first time. The underlying sparsity properties of the location data are explored and two algorithms based on LASSO regression and neural networks are shown to be able to efficiently reconstruct paths with only ∼20% sampling of the GPS receiver. An implementation for iOS devices is discussed and results from it are shown as proof of concept of the applicability of CS in location-based tracking for Internet of Things (IoT) devices.
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Badings, Thom S., Nils Jansen, Sebastian Junges, Marielle Stoelinga, and Matthias Volk. "Sampling-Based Verification of CTMCs with Uncertain Rates." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_2.

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AbstractWe employ uncertain parametric CTMCs with parametric transition rates and a prior on the parameter values. The prior encodes uncertainty about the actual transition rates, while the parameters allow dependencies between transition rates. Sampling the parameter values from the prior distribution then yields a standard CTMC, for which we may compute relevant reachability probabilities. We provide a principled solution, based on a technique called scenario-optimization, to the following problem: From a finite set of parameter samples and a user-specified confidence level, compute predicti
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Liubartseva, Svitlana, Giovanni Coppini, Pierre Daniel, and Megi Hoxhaj. "Modeling the high-impact-low-probability oil spills in the Mediterranean." In Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques. Firenze University Press, 2024. https://doi.org/10.36253/979-12-215-0556-6.79.

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Despite considerable efforts to improve scientific understanding and risk management, governments and businesses remain insufficiently prepared to confront large oil spills considered to be the so called ‘high-impact low-probability’ disasters. To alleviate this problem, we focus on the historical HAVEN oil spill (off the Port of Genoa, 1991) recognized not only as the largest shipwreck in the European waters, but also as one of the worst oil pollution cases in the Med. We reconstruct this spill with the Lagrangian oil spill model MEDSLIK-II forced by the to-date high resolution meteo-oceanogr
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Aslett, Louis J. M. "Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers." In Uncertainty in Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_2.

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AbstractModels which are constructed to represent the uncertainty arising in engineered systems can often be quite complex to ensure they provide a reasonably faithful reflection of the real-world system. As a result, even computation of simple expectations, event probabilities, variances, or integration over utilities for a decision problem can be analytically intractable. Indeed, such models are often sufficiently high dimensional that even traditional numerical methods perform poorly. However, access to random samples drawn from the probability model under study typically simplifies such pr
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Eckman, Stephanie, and Kristen Himelein. "Innovative Sample Designs for Studies of Refugees and Internally Displaced Persons." In IMISCOE Research Series. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01319-5_2.

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AbstractWith record numbers of refugees and internally displaced persons in the world, it is more important than ever that policy makers, aid organizations, and advocacy groups have access to high-quality data about these vulnerable populations. However, refugee and internally-displaced persons settlements pose unique challenges to the selection of probability samples. These settlements can grow quickly, and registers often are not available or not up-to-date. Refugees who live in communities also are difficult to reach with a probability sample because they are hard to identify, contact, and
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Yi, Pu, Hao Wang, Tao Xie, Darko Marinov, and Wing Lam. "A Theoretical Analysis of Random Regression Test Prioritization." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_12.

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AbstractRegression testing is an important activity to check software changes by running the tests in a test suite to inform the developers whether the changes lead to test failures. Regression test prioritization (RTP) aims to inform the developers faster by ordering the test suite so that tests likely to fail are run earlier. Many RTP techniques have been proposed and are often compared with the random RTP baseline by sampling some of the n! different test-suite orders for a test suite with n tests. However, there is no theoretical analysis of random RTP. We present such an analysis, derivin
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Yuniawan, Ahyar, Fuad Mas’ud, and Intan Ratnawati. "The Effect of Spiritual Leadership, Organizational Support, and Islamic Work Ethic." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_64.

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AbstractEmployees and companies are two things that cannot be separated because employees play a major role in carrying out company activities. Every company tries to improve and develop its company by doing many ways related to the employee performance improvement program. To make progress and achieve the goals set, a company needs to mobilize or monitor its employees to develop its capabilities. Motivation is the driving force for work, achievement and the basis for efforts to design attractive works so that employees want to do their jobs. The internal functions of the workforce/employees a
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Bojorque, Rodolfo, Andrea Plaza, and Pilar Morquecho. "Random Walks Sampling on the Facebook Network of the Massachusetts Institute of Technology Using Ant Colonies." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_3.

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Abstract This study investigates the effectiveness of using Ant Colony Optimization (ACO) algorithms for random walks sampling in the Facebook network of the Massachusetts Institute of Technology (MIT). Random walks sampling is a crucial technique for network analysis, enabling an understanding of the network’s state irrespective of the starting node. By implementing an ACO algorithm, this research demonstrates an efficient method of sampling that ensures all nodes are sampled with uniform probability. The ACO algorithm leverages heuristic methods to significantly reduce the warm-up time requi
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Lestari, Dian Mahardi, Isfenti Sadalia, and Amlys Syahputra Silalahi. "The Effect of Financial Literacy, Risk Perception, Overconfidence, and Investment Experience on Cryptocurrency Investment Decision." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_21.

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AbstractAn investment decision is a discretion adopted by investors in selecting or determining the placement of their investment funds. Investments are always fraught with uncertainty, as the investment occurs in the present while the benefits come in the future. Investment is essentially the deployment of capital now to gain profit in the future. This study aims to examine the effect of financial literacy, risk perception, and overconfidence on cryptocurrency investment decisions while taking investment experience as a moderating variable. The data used were primary data collected directly f
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Kim, Jeankyung, Jinsoo Hwang, Byungryong Kim, and Kichang Kim. "Proving Optimality of DWS(Distance-Weighted Sampling) Probability Function for FMS IP Trace-Back Technique." In Distributed Computing and Networking. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11947950_15.

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Conference papers on the topic "Probability Sampling Techniques"

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Guibert, Alexandre, Álvaro Díaz-Flores, Anirban Chaudhuri, and H. Alicia Kim. "Multifidelity Uncertainty Quantification in Battery Performance for eVTOL Flights Under Material and Loading Uncertainties." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1167.

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This study addresses safety concerns within the rapidly evolving Electric Vertical Takeoff and Landing (eVTOL) aircraft domain, focusing on efficient tools to quantify uncertainties in lithium-ion battery behavior - a critical aspect of eVTOL. One major issue with quantifying uncertainty is the prohibitive computational cost associated with many queries of an expensive-to-evaluate computational model. This work employs three physics-based battery models models of varying fidelity and cost to estimate the mean and the variance of the selected quantities of interest through a multifidelity metho
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Kala, Zdeněk. "Global sensitivity analysis of failure probability caused by fatigue crack propagation." In The 13th international scientific conference “Modern Building Materials, Structures and Techniques”. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/mbmst.2019.054.

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The probability of failure of a load bearing steel member is investigated using a new type of global sensitivity analysis subordinated to contrasts. The main objective of the probability-oriented sensitivity analysis is structural reliability. The structural reliability methodology uses random variables as inputs. The subject of interest is the identification of those random variables that are most important when the limit state of a steel bridge member is reached. The limit state is defined by the occurrence of brittle fracture, which results from stress changes caused by multiple repeated lo
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Mallahzadeh, H., Y. Wang, M. K. Abu Husain, N. I. Mohd Zaki, and G. Najafian. "Efficient Derivation of the Probability Distribution of Extreme Responses due to Random Wave Loading From the Probability Distribution of Extreme Surface Elevations." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10917.

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Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. Due to nonlinearity of the drag component of Morison’s wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of the probability distribution of extreme responses are not available. To this end, the conventional Monte Carlo time simulation technique is frequen
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Millwater, H. R., A. J. Smalley, Y. T. Wu, T. Y. Torng, and B. F. Evans. "Computational Techniques for Probabilistic Analysis of Turbomachinery." In ASME 1992 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/92-gt-167.

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This paper reports on some advanced computational techniques for probabilistic analysis of turbomachinery. A description of the requirements for probabilistic analysis and several solution methods are summarized. The traditional probabilistic analysis method, Monte Carlo simulation, and two advanced techniques, the Advanced Mean Value (AMV) method and importance sampling, are discussed. The performance of the Monte Carlo, AMV, and importance sampling methods is explored through a forced response analysis of a two degree-of-freedom Jeffcott rotor model. Variations in rotor weight, shaft length,
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Abu Husain, M. K., N. I. Mohd Zaki, and G. Najafian. "Prediction of Extreme Values of Offshore Structural Response by an Efficient Time Simulation Technique." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23126.

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Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. Due to nonlinearity of the drag component of Morison’s wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of the probability distribution of extreme responses are not available. To this end, the conventional Monte Carlo simulation technique (CTS) is freque
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Mallahzadeh, H., Y. Wang, M. K. Abu Husain, N. I. Mohd Zaki, and G. Najafian. "Accurate Estimation of the 100-Year Responses From the Probability Distribution of Extreme Surface Elevations." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24589.

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Accurate estimation of the 100-year responses (derived from the long-term distribution of extreme responses) is required for the safe and economical design of offshore structures. However, due to nonlinearity of the drag component of Morison’s wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of the probability distribution of extreme responses are not available. To this end, conventional Monte Carlo time simulation technique could be used for predicting the long-term probabi
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Zou, Tong, Sankaran Mahadevan, and Akhil Sopory. "A Reliability-Based Design Method Using Simulation Techniques and Efficient Optimization Approach." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57457.

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A novel reliability-based design optimization (RBDO) method using simulation-based techniques for reliability assessments and efficient optimization approach is presented in this paper. In RBDO, model-based reliability analysis needs to be performed to calculate the probability of not satisfying a reliability constraint and the gradient of this probability with respect to each design variable. Among model-based methods, the most widely used in RBDO is the first-order reliability method (FORM). However, FORM could be inaccurate for nonlinear problems and is not applicable for system reliability
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Marti, K. "Approximation and Derivatives of Probability Functions in Probabilistic Structural Analysis and Design." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0048.

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Abstract Yield stresses, allowable stresses, moment capacities (plastic moments), external loadings, manufacturing errors are not given fixed quantities in practice, but have to be modelled as random variables with a certain joint probability distribution. Hence, problems from limit (collapse) load analysis or plastic analysis and from plastic and elastic design of structures are treated in the framework of stochastic optimization. Using especially reliability-oriented optimization methods, the behavioral constraints are quantified by means of the corresponding probability ps of survival. Lowe
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Norouzi, Mahdi, and Badal Thapa. "Importance Sampling Combined With Separable Monte Carlo for Efficient Reliability Analysis of Linear Dynamic Structures." In ASME 2024 Aerospace Structures, Structural Dynamics, and Materials Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/ssdm2024-120912.

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Abstract The reliability of a structure under random dynamic load could be measured by estimating the probability of its response, e.g., a stress element or a node displacement, passing a threshold. Such analyses often require time domain simulation, especially for nonlinear systems. For systems with high reliability with a probability of failure less than 10−4, if Monte Carlo simulation (MCS) is used, an enormous number of replications must be completed which becomes impractical for real-life structures. Variance reduction techniques such as Importance Sampling (IS) or other methods such as S
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Muftuoglu, K., K. Ohkawa, C. Frepoli, and M. Nissley. "Comparison of Realistic Large Break LOCA Analyses of a 3-Loop Westinghouse Plant Using Response Surface and Statistical Sampling Techniques." In 12th International Conference on Nuclear Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/icone12-49499.

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In 1988, the U.S. Nuclear Regulatory Commission (NRC) issued a revision to 10CFR50.46 which allows realistic calculations for loss-of-coolant accident/emergency core cooling system (ECCS) analysis. The revision also requires that uncertainties in the analysis method and inputs be considered, such that there is a high level of probability that the ECCS criteria would not be exceeded. Currently, two methodologies are approved by the U.S. Nuclear Regulatory Commission for such calculations. One technique relies on response surface generation and applying Monte-Carlo technique to the regression mo
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