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Journal articles on the topic 'Sampling event'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kvist, Kajsa, Per Kragh Andersen, Jules Angst, and Lars Vedel Kessing. "Event dependent sampling of recurrent events." Lifetime Data Analysis 16, no. 4 (2010): 580–98. http://dx.doi.org/10.1007/s10985-010-9172-y.

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2

Yevick, David. "Accelerated rare event sampling." International Journal of Modern Physics C 27, no. 04 (2016): 1650041. http://dx.doi.org/10.1142/s0129183116500418.

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3

Bouchet, Freddy, Joran Rolland, and Jeroen Wouters. "Rare Event Sampling Methods." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 8 (2019): 080402. http://dx.doi.org/10.1063/1.5120509.

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4

Kim, Joocheol. "Event tree based sampling." Computers & Operations Research 33, no. 5 (2006): 1184–99. http://dx.doi.org/10.1016/j.cor.2004.09.008.

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5

Wichert, Andreas. "Quantum-Like Sampling." Mathematics 9, no. 17 (2021): 2036. http://dx.doi.org/10.3390/math9172036.

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Probability theory is built around Kolmogorov’s axioms. To each event, a numerical degree of belief between 0 and 1 is assigned, which provides a way of summarizing the uncertainty. Kolmogorov’s probabilities of events are added, the sum of all possible events is one. The numerical degrees of belief can be estimated from a sample by its true fraction. The frequency of an event in a sample is counted and normalized resulting in a linear relation. We introduce quantum-like sampling. The resulting Kolmogorov’s probabilities are in a sigmoid relation. The sigmoid relation offers a better importabi

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6

Ny, Jerome Le, and Sandra Hirche. "Differentially Private Event-Triggered Sampling." IFAC-PapersOnLine 52, no. 20 (2019): 303–8. http://dx.doi.org/10.1016/j.ifacol.2019.12.172.

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7

Kafle, Gopi Krishna, HungSoo Joo, and Pius M. Ndegwa. "Sampling Duration and Frequency for Determining Emission Rates from Naturally Ventilated Dairy Barns." Transactions of the ASABE 61, no. 2 (2018): 681–91. http://dx.doi.org/10.13031/trans.12543.

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Abstract. The ideal practice for determining gas emission rates from concentrated animal feeding operations (CAFOs) is continuous measurement throughout the year to capture diurnal and seasonal variations. However, data that meet this criterion are scarce because this approach is costly and technically challenging. A practical approach with reduced sampling time and frequency without compromising integrity is thus necessary. In this study, we examined five reduced sampling protocols for determining emission rates from naturally ventilated dairy barns: (1) six sampling events, during even month

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8

Rydzewski, J., and W. Nowak. "Rare-event sampling in ligand diffusion." Physics of Life Reviews 22-23 (December 2017): 85–87. http://dx.doi.org/10.1016/j.plrev.2017.08.011.

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9

Wu, Tzu-Fan, and Mike Shuo-Wei Chen. "A Subranging-Based Nonuniform Sampling ADC With Sampling Event Filtering." IEEE Solid-State Circuits Letters 1, no. 4 (2018): 78–81. http://dx.doi.org/10.1109/lssc.2018.2838527.

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10

Abbot, Dorian S., Robert J. Webber, Sam Hadden, Darryl Seligman, and Jonathan Weare. "Rare Event Sampling Improves Mercury Instability Statistics." Astrophysical Journal 923, no. 2 (2021): 236. http://dx.doi.org/10.3847/1538-4357/ac2fa8.

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Abstract Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury’s orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a M

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11

Zeren, Andrea S., and Vivian Parker Makosky. "Teaching Observational Methods: Time Sampling, Event Sampling, and Trait Rating Techniques." Teaching of Psychology 13, no. 2 (1986): 80–82. http://dx.doi.org/10.1207/s15328023top1302_8.

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This in-class activity permits the systematic observation of spontaneous human behavior as simulated on television and provides one effective way to demonstrate and compare time sampling, event sampling, and trait rating techniques. Students responded favorably to this activity, and many reported that it increased their understanding of the different observational techniques.

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12

Fermi, Jose, Sylvain Durand, Nicolas Marchand, and Guerrero Castellanos. "Simple Lyapunov Sampling for Event-Driven Control." IFAC Proceedings Volumes 44, no. 1 (2011): 8724–30. http://dx.doi.org/10.3182/20110828-6-it-1002.02396.

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13

Prellberg, Thomas. "Rare event sampling with stochastic growth algorithms." EPJ Web of Conferences 44 (2013): 01001. http://dx.doi.org/10.1051/epjconf/20134401001.

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14

Allen, Rosalind J., Chantal Valeriani, and Pieter Rein ten Wolde. "Forward flux sampling for rare event simulations." Journal of Physics: Condensed Matter 21, no. 46 (2009): 463102. http://dx.doi.org/10.1088/0953-8984/21/46/463102.

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15

Backes, Walter, and Pim van Dijk. "Simultaneous sampling of event-related BOLD responses." NeuroImage 13, no. 6 (2001): 2. http://dx.doi.org/10.1016/s1053-8119(01)91345-x.

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16

Cea, Mauricio G., and Graham C. Goodwin. "Event based sampling in non-linear filtering." Control Engineering Practice 20, no. 10 (2012): 963–71. http://dx.doi.org/10.1016/j.conengprac.2011.11.008.

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17

Öllös, Gergely, and Rolland Vida. "Adaptive multiresolution sampling in event-driven WSNs." Telecommunication Systems 61, no. 2 (2015): 337–47. http://dx.doi.org/10.1007/s11235-015-0005-x.

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18

Johnson, Cindy. "Decision '08: event marketing or product sampling?" Journal of Consumer Marketing 25, no. 5 (2008): 269–71. http://dx.doi.org/10.1108/07363760810890499.

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19

Jaskulke, R., and B. Himmel. "Event-Controlled Sampling System for Marine Research." IEEE Transactions on Instrumentation and Measurement 54, no. 3 (2005): 1175–79. http://dx.doi.org/10.1109/tim.2005.847142.

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20

Marin-Castro, Heidy M., Miguel Morales-Sandoval, José Luis González-Compean, and Julio Hernandez. "A novel trace-based sampling method for conformance checking." PeerJ Computer Science 10 (December 18, 2024): e2601. https://doi.org/10.7717/peerj-cs.2601.

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It is crucial for organizations to ensure that their business processes are executed accurately and comply with internal policies and requirements. Process mining is a discipline of data science that exploits business process execution data to analyze and improve business processes. It provides a data-driven approach to understanding how processes actually work in practice. Conformance checking is one of the three most relevant process mining tasks. It consists of determining the degree of correspondence or deviation between the expected (or modeled) behavior of a process vs the real one obser

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21

Molina, Javier, Federico Mendez, Dax Kelly, David Martin, and Doug Palmer. "A Practical Approach to Publishing and Using Event-based Biodiversity Data." Biodiversity Information Science and Standards 6 (August 23, 2022): e91512. https://doi.org/10.3897/biss.6.91512.

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Despite the rapid growth of biodiversity data within global and national biodiversity infrastructures, the types of data available in these infrastructures are limited. In particular, the lack of data that captures presences/absences of species over multiple survey or sampling events has been identified as a major weakness of the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA).The ALA, in collaboration with GBIF, have started work to deliver improved access to data types in the short-term by focusing on event-based biodiversity data, like from expedition

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22

Lerner, Jürgen, and Alessandro Lomi. "Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events." Network Science 8, no. 1 (2019): 97–135. http://dx.doi.org/10.1017/nws.2019.57.

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AbstractWe assess the reliability of relational event model (REM) parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case–control sampling which samples nonevents, or null dyads (“controls”), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that REMs can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of som

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23

McLeish, Don L. "Bounded Relative Error Importance Sampling and Rare Event Simulation." ASTIN Bulletin 40, no. 1 (2010): 377–98. http://dx.doi.org/10.2143/ast.40.1.2049235.

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AbstractWe consider estimating tail events using exponential families of importance sampling distributions. When the cannonical sufficient statistic for the exponential family mimics the tail behaviour of the underlying cumulative distribution function, we can achieve bounded relative error for estimating tail probabilities. Examples of rare event simulation from various distributions including Tukey's g&h distribution are provided.

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24

Wood, Emma H., and Jonathan Moss. "Capturing emotions: experience sampling at live music events." Arts and the Market 5, no. 1 (2015): 45–72. http://dx.doi.org/10.1108/am-02-2013-0002.

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Purpose – Using techniques developed mainly in subjective well-being and “happiness” studies, the purpose of this paper is to discuss the applicability of these and related methods for understanding and evaluating the emotional responses experienced within the live music event environment. Design/methodology/approach – The concept of “experience” is debated and set within the context of music events designed to create a specific type of emotional experience for the attendees. The main tools for researching experiences over a time period are considered focusing on the “experience sampling metho

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25

HARADA, Ryuhei, Vladimir SLADEK, and Yasuteru SHIGETA. "Developments of Rare Event Sampling Methods for Proteins." Journal of Computer Chemistry, Japan 18, no. 5 (2019): 199–201. http://dx.doi.org/10.2477/jccj.2019-0036.

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26

Burgan, B., and T. Mules. "Sampling Frame Issues in Identifying Event-Related Expenditure." Event Management 6, no. 4 (2000): 223–30. http://dx.doi.org/10.3727/152599500108751381.

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27

Cheng, Kai, Iason Papaioannou, Zhenzhou Lu, Xiaobo Zhang, and Yanping Wang. "Rare event estimation with sequential directional importance sampling." Structural Safety 100 (January 2023): 102291. http://dx.doi.org/10.1016/j.strusafe.2022.102291.

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28

Denny, Mark. "Introduction to importance sampling in rare-event simulations." European Journal of Physics 22, no. 4 (2001): 403–11. http://dx.doi.org/10.1088/0143-0807/22/4/315.

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29

Webber, Robert J., David A. Plotkin, Morgan E. O’Neill, Dorian S. Abbot, and Jonathan Weare. "Practical rare event sampling for extreme mesoscale weather." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 5 (2019): 053109. http://dx.doi.org/10.1063/1.5081461.

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30

Song, Jingwen, Pengfei Wei, Marcos Valdebenito, and Michael Beer. "Active learning line sampling for rare event analysis." Mechanical Systems and Signal Processing 147 (January 2021): 107113. http://dx.doi.org/10.1016/j.ymssp.2020.107113.

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31

Qu, Liang, Zhang Wu, Michael B. C. Khoo, and Abdur Rahim. "Time-Between-Event Control Charts for Sampling Inspection." Technometrics 56, no. 3 (2014): 336–46. http://dx.doi.org/10.1080/00401706.2013.841592.

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32

Doll, J. D., J. E. Gubernatis, Nuria Plattner, Markus Meuwly, P. Dupuis, and H. Wang. "A spatial averaging approach to rare-event sampling." Journal of Chemical Physics 131, no. 10 (2009): 104107. http://dx.doi.org/10.1063/1.3220629.

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33

Wagner, F., J. Latz, I. Papaioannou, and E. Ullmann. "Multilevel Sequential Importance Sampling for Rare Event Estimation." SIAM Journal on Scientific Computing 42, no. 4 (2020): A2062—A2087. http://dx.doi.org/10.1137/19m1289601.

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34

Blumstein, Alfred, José A. Canela-Cacho, and Jacqueline Cohen. "Filtered Sampling from Populations with Heterogeneous Event Frequencies." Management Science 39, no. 7 (1993): 886–99. http://dx.doi.org/10.1287/mnsc.39.7.886.

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35

Kratzer, Kai, Joshua T. Berryman, Aaron Taudt, Johannes Zeman, and Axel Arnold. "The Flexible Rare Event Sampling Harness System (FRESHS)." Computer Physics Communications 185, no. 7 (2014): 1875–85. http://dx.doi.org/10.1016/j.cpc.2014.03.013.

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36

Yu, Hao, and Tongwen Chen. "Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times." Automatica 167 (September 2024): 111798. http://dx.doi.org/10.1016/j.automatica.2024.111798.

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37

Aldira, Kelvin, I. Made Sendra, and LGLK Dewi. "PENERAPAN PROTOKOL CHSE PADA CENTRAL EVENT ORGANIZER SAAT KENORMALAN BARU." Jurnal IPTA 10, no. 2 (2023): 307. http://dx.doi.org/10.24843/ipta.2022.v10.i02.p15.

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This study focuses on the application of the CHSE (Cleanliness, Health, Safety, and Environment Sustainability) protocol in organizing events at the Central Event Organizer during the new normal. The Covid-19 pandemic has a direct impact on the event organizing industry, where the level of public trust in the event organizing company has decreased, as well as the assumption that organizing events is the cause of mass crowds. The purpose of this study is to find out how the implementation of the CHSE health protocol (Cleanliness, Health, Safety, and Environment Sustainability) at events organiz

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38

Pennock, D. J., T. T. Yates, and J. T. Braidek. "Towards optimum sampling for regional-scale N2O emission monitoring in Canada." Canadian Journal of Soil Science 86, no. 3 (2006): 441–50. http://dx.doi.org/10.4141/s05-104.

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There is an increasing need for field monitoring studies of N2O emissions to assess the reliability of process models. Our goal is to review the issues surrounding the design of monitoring and regional upscaling of fieldmeasured N2O emissions for Canadian conditions. Management history creates a range of controlling conditions and emission responses for each land use present in the study region and multiple fields should be sampled within each land use class. The requirement for multiple sample fields necessitates chamber-based sampling designs (ideally in conjunction with site-specific microm

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39

Roser, D., J. Skinner, C. LeMaitre, et al. "Automated event sampling for microbiological and related analytes in remote sites: a comprehensive system." Water Supply 2, no. 3 (2002): 123–30. http://dx.doi.org/10.2166/ws.2002.0094.

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Pathogen concentrations are most often monitored during dry weather. Generally in Australia, however, loads mobilised during storms are of more concern. The filling of reservoirs commonly occurs from heavy rain events, and flood inputs may destabilise reservoir hydraulics leading to short-circuiting of contaminates to water supply off-takes. To capture storm events that can occur rapidly in remote locations at any time, automated sampling would seem appropriate. Unfortunately no commercial sampling system appears suitable for collecting multiple large volume samples along a hydrograph. We repo

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40

Benson, Abigail. "Improving data availability with "OBIS-ENV-DATA": Examples from OBIS-USA." Biodiversity Information Science and Standards 1 (August 11, 2017): e20207. https://doi.org/10.3897/tdwgproceedings.1.20207.

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During biological sampling events, measurements are routinely collected about the event as well as about the biological observations. For example, the same sampling event might collect event measurements like water temperature and salinity as well as biological measurements like abundance and weight. Keeping these measurements together is important to be able to assess how species might be responding to changes in their environment and to be able to make predictions into the future. However, the implementation of Darwin Core utilized on the Integrated Publishing Toolkit (IPT), open-source soft

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41

Stadler, H., P. Skritek, R. Sommer, R. L. Mach, W. Zerobin, and A. H. Farnleitner. "Microbiological monitoring and automated event sampling at karst springs using LEO-satellites." Water Science and Technology 58, no. 4 (2008): 899–909. http://dx.doi.org/10.2166/wst.2008.442.

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Data communication via Low-Earth-Orbit (LEO) Satellites between portable hydrometeorological measuring stations is the backbone of our system. This networking allows automated event sampling with short time increments also for E. coli field analysis. All activities of the course of the event-sampling can be observed on an internet platform based on a Linux-Server. Conventionally taken samples compared with the auto-sampling procedure revealed corresponding results and were in agreement with the ISO 9308-1 reference method. E. coli concentrations were individually corrected by event specific in

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42

Smith, Peter J. "Underestimation of Rare Event Probabilities in Importance Sampling Simulations." SIMULATION 76, no. 3 (2001): 140–50. http://dx.doi.org/10.1177/003754970107600301.

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43

Sánchez, J., S. Dormido-Canto, J. Vega, N. Duro, R. Dormido, and S. Dormido. "Application of Event-Based Sampling Strategies for Fusion Research." Fusion Science and Technology 58, no. 2 (2010): 666–74. http://dx.doi.org/10.13182/fst10-a10891.

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44

Yevick, David, and Yong Hwan Lee. "Accelerated rare event sampling: Refinement and Ising model analysis." International Journal of Modern Physics C 28, no. 01 (2017): 1750012. http://dx.doi.org/10.1142/s0129183117500127.

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In this paper, a recently introduced accelerated sampling technique [D. Yevick, Int. J. Mod. Phys. C 27, 1650041 (2016)] for constructing transition matrices is further developed and applied to a two-dimensional [Formula: see text] Ising spin system. By permitting backward displacements up to a certain limit for each forward step while evolving the system to first higher and then lower energies within a restricted interval that is steadily displaced toward zero temperature as the computation proceeds, accuracy can be greatly enhanced. Simultaneously, the elements obtained from numerous indepen

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45

Latz, Jonas, Doris Schneider, and Philipp Wacker. "Nested Sampling for Uncertainty Quantification and Rare Event Estimation." SIAM Journal on Scientific Computing 46, no. 5 (2024): A3305—A3329. http://dx.doi.org/10.1137/23m1607842.

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46

Fan, Yuan, Yong Yang, and Yang Zhang. "Sampling-based event-triggered consensus for multi-agent systems." Neurocomputing 191 (May 2016): 141–47. http://dx.doi.org/10.1016/j.neucom.2015.12.102.

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47

Hashimoto, Kazumune, Shuichi Adachi, and Dimos V. Dimarogonas. "Event-triggered intermittent sampling for nonlinear model predictive control." Automatica 81 (July 2017): 148–55. http://dx.doi.org/10.1016/j.automatica.2017.03.028.

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48

Tse, Margaret J., Brian K. Chu, Cameron P. Gallivan, and Elizabeth L. Read. "Rare-event sampling of epigenetic landscapes and phenotype transitions." PLOS Computational Biology 14, no. 8 (2018): e1006336. http://dx.doi.org/10.1371/journal.pcbi.1006336.

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49

Salins, Michael, and Konstantinos Spiliopoulos. "Rare event simulation via importance sampling for linear SPDE’s." Stochastics and Partial Differential Equations: Analysis and Computations 5, no. 4 (2017): 652–90. http://dx.doi.org/10.1007/s40072-017-0100-y.

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50

Gong, Hang, Fangke Wu, Runzhi Liu, Xin Jin, Wei Zhou, and Xing Chu. "Event-Based Sampled-Data Average Consensus." Mathematical Problems in Engineering 2020 (July 18, 2020): 1–13. http://dx.doi.org/10.1155/2020/1586289.

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This study addresses one of the most essential distributed control problems in multiagent systems, called the average consensus issue, using a new event-triggered sampling control perspective. Although the continuous-time sampling for average consensus has provided good results currently, a systematic investigation into the continuous-time agent dynamics with sampled-data control inputs under an event-triggered mechanism is critically lacking. The problem considered in this paper can be formulated into an average consensus problem of hybrid systems. The method considers three types of control

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