Relevant bibliographies by topics / Test de Monte Carlo

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

Academic literature on the topic 'Test de Monte Carlo'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Test de Monte Carlo"

1

Ohta, Shigemi. "Self-Test Monte Carlo Method." Progress of Theoretical Physics Supplement 122 (1996): 193–200. http://dx.doi.org/10.1143/ptps.122.193.

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Oht, Shigemi. "Self-test Monte Carlo method." Nuclear Physics B - Proceedings Supplements 47, no. 1-3 (March 1996): 788–91. http://dx.doi.org/10.1016/0920-5632(96)00175-2.

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Gangur, Mikuláš, and Milan Svoboda. "Simulation of Bayes' rule by means of Monte Carlo method." Teaching Statistics 40, no. 3 (April 6, 2018): 83–87. http://dx.doi.org/10.1111/test.12158.

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Silva, Ivair R., and Renato M. Assunção. "Optimal generalized truncated sequential Monte Carlo test." Journal of Multivariate Analysis 121 (October 2013): 33–49. http://dx.doi.org/10.1016/j.jmva.2013.06.003.

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Silva, I., R. Assunção, and M. Costa. "Power of the Sequential Monte Carlo Test." Sequential Analysis 28, no. 2 (April 27, 2009): 163–74. http://dx.doi.org/10.1080/07474940902816601.

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Theil, Henri, J. S. Shonkwiler, and Timothy G. Taylor. "A Monte Carlo test of Slutsky symmetry." Economics Letters 19, no. 4 (January 1985): 331–32. http://dx.doi.org/10.1016/0165-1765(85)90230-7.

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Selvanathan, Saroja. "A Monte Carlo test of preference independence." Economics Letters 25, no. 3 (January 1987): 259–61. http://dx.doi.org/10.1016/0165-1765(87)90224-2.

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Piras, Gianfranco, and Nancy Lozano-Gracia. "Spatial J-test: some Monte Carlo evidence." Statistics and Computing 22, no. 1 (November 6, 2010): 169–83. http://dx.doi.org/10.1007/s11222-010-9215-y.

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Hunt, R. A., D. R. Dance, M. Pachoud, G. Alm Carlsson, M. Sandborg, G. Ullman, and F. R. Verdun. "Monte Carlo simulation of a mammographic test phantom." Radiation Protection Dosimetry 114, no. 1-3 (May 17, 2005): 432–35. http://dx.doi.org/10.1093/rpd/nch511.

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Vishnyakov, Aleksey, and Alexander V. Neimark. "Monte Carlo Simulation Test of Pore Blocking Effects." Langmuir 19, no. 8 (April 2003): 3240–47. http://dx.doi.org/10.1021/la0269107.

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Dissertations / Theses on the topic "Test de Monte Carlo"

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Ding, Jie. "Monte Carlo Pedigree Disequilibrium Test with Missing Data and Population Structure." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218475579.

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Sheytanova, Teodora. "The Accuracy of the Hausman Test in Panel Data: a Monte Carlo Study." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-44288.

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Yeung, Alan B. (Alan Brian) Carleton University Dissertation Physics. "A Monte Carlo study of the Sudbury Neutrino Observatory small test detector experiment." Ottawa, 1990.

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Xie, Wen. "A Monte Carlo Simulation Study for Poly-k Test in Animal Carcinogenicity Studies." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10638898.

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An objective of animal carcinogenicity studies is to identify a tumorigenic potential in animals and to assess relevant risks in humans. Without using the cause-of-death information, the Cochran-Armitage test is applied for detecting a linear trend in the incidence of a tumor of interest across dose groups. The survival-adjusted Cochran–Armitage test, known as the Poly-k test, is investigated for the animals not at equal risk of tumor development by reflecting the shapes of the tumor onset distributions. In this thesis, we will validate Poly-k test through a Monte Carlo simulation study. We will design the simulation study to assess the size and power of the poly-k test using a wide range of k values for various tumor onset rates, for various competing risks rates, and for various tumor lethality rates. In this thesis, the Poly-k testing approach will be investigated to evaluate a dose-related linear trend of a test subject on the incidence of tumor and will be implemented in R package to be used widely amongst toxicologists.

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Leite, Nelson Paiva Oliveira, and Lucas Benedito dos Reis Sousa. "Uncertainty Determination with Monte-Carlo Based Algorithm." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595756.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
The measurement result is complete only if it contains the measurand and its units, uncertainty and coverage factor. The uncertainty estimation for the parameters acquired by the FTI is a known process. To execute this task the Institute of Research and Flight Test (IPEV) developed the SALEV© system which is fully compliant with the applicable standards. But the measurement set also includes Derived Parameters. The uncertainty evaluation of these parameters can be solved by cumbersome partial derivates. The search for a simpler solution leads us to a Monte-Carlo based algorithm. The result of using this approach are presented and discussed.
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Georgii, Hellberg Kajsa-Lotta, and Andreas Estmark. "Fisher's Randomization Test versus Neyman's Average Treatment Test." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385069.

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The following essay describes and compares Fisher's Randomization Test and Neyman's average treatment test, with the intention of concluding an easily understood blueprint for the comprehension of the practical execution of the tests and the conditions surrounding them. Focus will also be directed towards the tests' different implications on statistical inference and how the design of a study in relation to assumptions affects the external validity of the results. The essay is structured so that firstly the tests are presented and evaluated, then their different advantages and limitations are put against each other before they are applied to a data set as a practical example. Lastly the results obtained from the data set are compared in the Discussion section. The example used in this paper, which compares cigarette consumption after having treated one group with nicotine patches and another with fake nicotine patches, shows a decrease in cigarette consumption for both tests. The tests differ however, as the result from the Neyman test can be made valid for the population of interest. Fisher's test on the other hand only identifies the effect derived from the sample, consequently the test cannot draw conclusions about the population of heavy smokers. In short, the findings of this paper suggests that a combined use of the two tests would be the most appropriate way to test for treatment effect. Firstly one could use the Fisher test to check if any effect at all exist in the experiment, and then one could use the Neyman test to compensate the findings of the Fisher test, by estimating an average treatment effect for example.
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Lindahl, John, and Douglas Persson. "Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43498.

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Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.
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Luo, Zhisui. "A Bayesian Analysis of a Multiple Choice Test." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/269.

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In a multiple choice test, examinees gain points based on how many correct responses they got. However, in this traditional grading, it is assumed that questions in the test are replications of each other. We apply an item response theory model to estimate students' abilities characterized by item's feature in a midterm test. Our Bayesian logistic Item response theory model studies the relation between the probability of getting a correct response and the three parameters. One parameter measures the student's ability and the other two measure an item's difficulty and its discriminatory feature. In this model the ability and the discrimination parameters are not identifiable. To address this issue, we construct a hierarchical Bayesian model to nullify the effects of non-identifiability. A Gibbs sampler is used to make inference and to obtain posterior distributions of the three parameters. For a "nonparametric" approach, we implement the item response theory model using a Dirichlet process mixture model. This new approach enables us to grade and cluster students based on their "ability" automatically. Although Dirichlet process mixture model has very good clustering property, it suffers from expensive and complicated computations. A slice sampling algorithm has been proposed to accommodate this issue. We apply our methodology to a real dataset obtained on a multiple choice test from WPI’s Applied Statistics I (Spring 2012) that illustrates how a student's ability relates to the observed scores.
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Senteney, Michael H. "A Monte Carlo Study to Determine Sample Size for Multiple Comparison Procedures in ANOVA." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160433478343909.

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Nguyen, Diep Thi. "Statistical Models to Test Measurement Invariance with Paired and Partially Nested Data: A Monte Carlo Study." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7869.

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While assessing emotions, behaviors or performance of preschoolers and young children, scores from adults such as parent psychiatrist and teacher ratings are used rather scores from children themselves. Data from parent ratings or from parents and teachers are often nested such as students are within teachers and a child is within their parents. This popular nested feature of data in educational, social and behavioral sciences makes measurement invariance (MI) testing across informants of children methodologically challenging. There was lack of studies that take into account the nested structure of data in MI testing for multiple adult informants, especially no simulation study that examines the performance of different models used to test MI across different raters. This dissertation focused on two specific nesting data types in testing MI between adult raters of children: paired and partial nesting. For the paired data, the independence assumption of regular MI testing is often violated because the two informants (e.g., father and mother) rate the same child and their scores are anticipated to be related or dependent. The partial nesting data refers to the research situation where teacher and parent ratings are compared. In this scenario, it is common that each parent has only one child to rate while each teacher has multiple children in their classroom. Thus, in case of teacher and parent ratings of the same children, data are repeated measures and also partially nested. Because of these unique features of data, MI testing between adult informants of children requires statistical models that take into account different types of data dependency. I proposed and evaluated the performance of the two statistical models that can handle repeated measures and partial nesting with several simulated research scenarios in addition to one commonly used and one potentially appropriate statistical models across several research scenario. Results of the two simulation studies in this dissertation showed that for the paired data, both multiple-group confirmatory factor analysis (CFA) and repeated measure CFA models were able to detect scalar invariance most of the time using Δχ2 test and ΔCFI. Although the multiple-group CFA (Model 2) was able to detect scalar invariance better than the repeated measure CFA model (Model 1), the detection rates of Model 1 were still at the high level (88% - 91% using Δχ2 test and 84% - 100% using ΔCFI or ΔRMSEA). For configural invariance and metric invariance conditions for the paired data, Model 1 had higher detection rate than Model 2 in almost examined research scenario in this dissertation. Particularly while Model 1 could detect noninvariance (either in intercepts only or in both intercepts and factor loadings) than Model 2 for paired data most of the time, Model 2 could rarely catch it if using suggested cut-off of 0.01 for RMSEA differences. For the paired data, although both Models 1 and 2 could be a good choice to test measurement invariance, Model 1 might be favored if researchers are more interested in detecting noninvariance due to its overall high detection rates for all three levels (i.e. configural, metric, and scalar) of measurement invariance. For scalar invariance with partially nested data, both multilevel repeated measure CFA and design-based multilevel CFA could detect invariance most of the time (from 81% to 100% of examined cases) with slightly higher detection rate for the former model than the later. Multiple-group CFA model hardly detect scalar invariance except when ICC was small. The detection rates for configural invariance using Δχ2 test or Satorra-Bentler LRT were also highest for Model 3 (82% to 100% except only two conditions with detection rates of 61%), following by Model 5 and lowest Model 4. Models 4 and 5 could reach these rates only with the largest sample sizes (i.e., large number of cluster or large cluster size or large in both factors) when the magnitude of noninvariance was small. Unlike scalar and configural invariance, the ability to detect metric invariance was highest for Model 4, following by Model 5 and lowest for Model 3 across many conditions using all of the three performance criteria. As higher detection rates for all configural and scalar invariance, and moderate detection rates for many metric invariance conditions (except cases of small number of clusters combined with large ICC), Model 3 could be a good candidate to test measurement invariance with partially nested data when having sufficient number of clusters or if having small number of clusters with small ICC. Model 5 might be also a reasonable option for this type of data if both the number of clusters and cluster size were large (i.e., 80 and 20, respectively), or either one of these two factors was large coupled with small ICC. If ICC is not small, it is recommended to have a large number of clusters or combination of large number of clusters and large cluster size to ensure high detection rates of measurement invariance for partially nested data. As multiple group CFA had better and reasonable detection rates than the design-based and multilevel repeated measure CFA models cross configural, metric and scalar invariance with the conditions of small cluster size (10) and small ICC (0.13), researchers can consider using this model to test measurement invariance when they can only collect 10 participants within a cluster (e.g. students within a classroom) and there is small degree of data dependency (e.g. small variance between clusters) in the data.
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Books on the topic "Test de Monte Carlo"

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Nonparametric Monte Carlo tests and their applications. New York: Springer, 2005.

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Koura, Katsuhisa. A sensitivity test for accuracy in evaluation of molecular collision number in the direct-simulation Monte Carlo method. Tokyo, Japan: National Aerospace Laboratory, 1990.

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Haug, Alfred A. Tests for cointegration: a Monte Carlo comparison. Toronto, Ont: York University, Department of Economics, 1993.

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Inoue, Atsushi. A portmanteau test for serially correlated errors in fixed effects models. Cambridge, MA: National Bureau of Economic Research, 2005.

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Morelli, Eugene A. Determining the accuracy of aerodynamic model parameters estimated from flight test data. Washington, D.C: American Institute of Aeronautics and Astronautics, 1995.

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Morelli, Eugene A. Determining the accuracy of aerodynamic model parameters estimated from flight test data. Washington, D.C: American Institute of Aeronautics and Astronautics, 1995.

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Morelli, Eugene A. Determining the accuracy of aerodynamic model parameters estimated from flight test data. Washington, D.C: American Institute of Aeronautics and Astronautics, 1995.

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Anderson, Gordon. Nonparametric tests for common but unspecified population distributions: A Monte Carlo comparison. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1994.

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Böhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.

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Scott, Louis O. The present value model of stock prices: Regression tests and Monte Carlo results. [Urbana]: College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1985.

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Book chapters on the topic "Test de Monte Carlo"

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Matsumoto, Makoto, and Takuji Nishimura. "A Nonempirical Test on the Weight of Pseudorandom Number Generators." In Monte Carlo and Quasi-Monte Carlo Methods 2000, 381–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56046-0_26.

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Haramoto, Hiroshi, Makoto Matsumoto, Takuji Nishimura, and Yuki Otsuka. "A Non-empirical Test on the Second to the Sixth Least Significant Bits of Pseudorandom Number Generators." In Monte Carlo and Quasi-Monte Carlo Methods 2012, 417–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41095-6_19.

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Chou, Wun-Seng, and Harald Niederreiter. "On the lattice test for inversive congruential pseudorandom numbers." In Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 186–97. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2552-2_10.

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Bod, Rens. "Monte Carlo Parsing." In Text, Speech and Language Technology, 255–80. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-010-9733-8_14.

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Schmolck, Björn. "Monte Carlo experimentation." In Omitted Variable Tests and Dynamic Specification, 75–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58324-7_6.

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Leopardi, Paul. "Testing the Tests: Using Random Number Generators to Improve Empirical Tests." In Monte Carlo and Quasi-Monte Carlo Methods 2008, 501–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04107-5_32.

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L’Ecuyer, Pierre. "Random Number Generators and Empirical Tests." In Monte Carlo and Quasi-Monte Carlo Methods 1996, 124–38. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1690-2_7.

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Ho, Chih-Hsiang. "A Simulation Study of a Change-Point Poisson Process Based on Two Well-known Test Statistics." In Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 228–38. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2552-2_14.

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Haramoto, Hiroshi. "Automation of Statistical Tests on Randomness to Obtain Clearer Conclusion." In Monte Carlo and Quasi-Monte Carlo Methods 2008, 411–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04107-5_26.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Monte Carlo tests and randomization." In Practical R for biologists: an introduction, 187–93. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0187.

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Abstract This chapter focuses on Monte Carlo tests and randomization. It involves randomizing the observed numbers many times and comparing the randomized results with the original observed data. It is shown how randomization can be used in experimental design and sampling.
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Conference papers on the topic "Test de Monte Carlo"

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Stratigopoulos, Haralampos-G., and Stephen Sunter. "Efficient Monte Carlo-based analog parametric fault modelling." In 2014 IEEE 32nd VLSI Test Symposium (VTS). IEEE, 2014. http://dx.doi.org/10.1109/vts.2014.6818741.

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GRIGORIEV, D. YU, E. JANKOWSKI, and F. V. TKACHOV. "A MONTE CARLO TEST OF THE OPTIMAL JET DEFINITION." In Proceedings of the Eighteenth Lake Louise Winter Institute. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702777_0023.

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Kanoria, Yashodhan, Subhasish Mitra, and Andrea Montanari. "Statistical static timing analysis using Markov chain Monte Carlo." In 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010). IEEE, 2010. http://dx.doi.org/10.1109/date.2010.5456938.

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Mingfu, Xue, Hu Aiqun, Huang Yi, and Li Guyue. "Monte Carlo Based Test Pattern Generation for Hardware Trojan Detection." In 2013 IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 2013. http://dx.doi.org/10.1109/dasc.2013.50.

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Sarmento, Rafaela, Ettore Segreto, Ana Machado, Henrique de Souza, Bruno Gelli, Marina Guzzo, Jully Nascimento, and Greg Souza. "Test of X–ARAPUCA through Monte Carlo simulation in Brazil." In Congresso de Iniciação Científica UNICAMP. Universidade Estadual de Campinas, 2019. http://dx.doi.org/10.20396/revpibic2720192106.

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Runkel, Michael J. "Monte Carlo simulation of the R/1 automated damage test." In Laser-Induced Damage in Optical Materials: 1998, edited by Gregory J. Exarhos, Arthur H. Guenther, Mark R. Kozlowski, Keith L. Lewis, and M. J. Soileau. SPIE, 1999. http://dx.doi.org/10.1117/12.344457.

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Leger, Gildas, and Manuel J. Barragan. "Questioning the reliability of Monte Carlo simulation for machine learning test validation." In 2016 IEEE European Test Symposium (ETS). IEEE, 2016. http://dx.doi.org/10.1109/ets.2016.7519307.

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Jaffari, Javid, and Mohab Anis. "Practical Monte-Carlo based timing yield estimation of digital circuits." In 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010). IEEE, 2010. http://dx.doi.org/10.1109/date.2010.5456941.

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Mahmoudi, Hiwa, and Horst Zimmermann. "A new sampling technique for Monte Carlo-based statistical circuit analysis." In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7927188.

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Prulhiere, Géraud, Bruno Fontaine, and Thomas Frosio. "Simulation of the core flowering End-of-life test realized on PHENIX reactor." In SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, edited by D. Caruge, C. Calvin, C. M. Diop, F. Malvagi, and J. C. Trama. Les Ulis, France: EDP Sciences, 2014. http://dx.doi.org/10.1051/snamc/201401403.

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Reports on the topic "Test de Monte Carlo"

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Davidson, J. R. Monte Carlo tests of the ELIPGRID-PC algorithm. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/52637.

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Schwert, G. William. Tests For Unit Roots: A Monte Carlo Investigation. Cambridge, MA: National Bureau of Economic Research, December 1988. http://dx.doi.org/10.3386/t0073.

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Chang, B. Six 1D Polar Transport Test Problems for the Deterministic Monte-Carlo Method. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1769096.

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Brown, Forrest B., and William Russell Martin. Statistical Tests for Convergence in Monte Carlo Criticality Calculations. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1471319.

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G. S. Chang and R. C. Pederson. Monte-Carlo Code (MCNP) Modeling of the Advanced Test Reactor Applicable to the Mixed Oxide (MOX) Test Irradiation. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/911248.

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Lo, Andrew, and A. Craig MacKinlay. The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation. Cambridge, MA: National Bureau of Economic Research, June 1988. http://dx.doi.org/10.3386/t0066.

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Remund, Todd, and William Kitto. Monte Carlo Techniques for Estimating Power in Aircraft T&E Tests. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada545255.

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Brown, Forrest B. Statistical Tests for Diagnosing Fission Source Convergence and Undersampling in Monte Carlo Criticality Calculations. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1638606.

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Vogel, Thomas. Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148317.

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Brown, F. B., and T. M. Sutton. Monte Carlo fundamentals. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/270327.

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