Academic literature on the topic 'Parameter uncertainty'
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Journal articles on the topic "Parameter uncertainty"
Kim, Eung Seok. "Analysis of Runoff According to Application of SWMM-LID Element Technology (II): Parameter Uncertainty Analysis." Journal of the Korean Society of Hazard Mitigation 20, no. 6 (December 31, 2020): 445–50. http://dx.doi.org/10.9798/kosham.2020.20.6.445.
Full textHøjberg, A. L., and J. C. Refsgaard. "Model uncertainty – parameter uncertainty versus conceptual models." Water Science and Technology 52, no. 6 (September 1, 2005): 177–86. http://dx.doi.org/10.2166/wst.2005.0166.
Full textChen, Si, Guoqi Xie, Renfa Li, and Keqin Li. "Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis." ACM Transactions on Design Automation of Electronic Systems 27, no. 3 (May 31, 2022): 1–19. http://dx.doi.org/10.1145/3490177.
Full textWeise, K. "Uncertainty of Parameter Estimation." IFAC Proceedings Volumes 18, no. 5 (July 1985): 1717–22. http://dx.doi.org/10.1016/s1474-6670(17)60816-4.
Full textPaulter, N. G., and D. R. Larson. "Pulse parameter uncertainty analysis." Metrologia 39, no. 2 (April 2002): 143–55. http://dx.doi.org/10.1088/0026-1394/39/2/4.
Full textWakeland, Wayne, and Jack Homer. "Addressing Parameter Uncertainty in a Health Policy Simulation Model Using Monte Carlo Sensitivity Methods." Systems 10, no. 6 (November 18, 2022): 225. http://dx.doi.org/10.3390/systems10060225.
Full textBai, Jie, Shuai Liu, and Wei Wang. "Study on Identification Method for Parameter Uncertainty Model of Aero Engine." International Journal of Aerospace Engineering 2019 (December 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/6015270.
Full textMousavi, S. Jamshid, K. C. Abbaspour, B. Kamali, M. Amini, and H. Yang. "Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach." Journal of Hydroinformatics 14, no. 2 (May 10, 2011): 286–309. http://dx.doi.org/10.2166/hydro.2011.071.
Full textPernot, Pascal. "The parameter uncertainty inflation fallacy." Journal of Chemical Physics 147, no. 10 (September 14, 2017): 104102. http://dx.doi.org/10.1063/1.4994654.
Full textGerrard, R., and A. Tsanakas. "Failure Probability Under Parameter Uncertainty." Risk Analysis 31, no. 5 (December 22, 2010): 727–44. http://dx.doi.org/10.1111/j.1539-6924.2010.01549.x.
Full textDissertations / Theses on the topic "Parameter uncertainty"
Sui, Liqi. "Uncertainty management in parameter identification." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.
Full textIn order to obtain more predictive and accurate simulations of mechanical behaviour in the practical environment, more and more complex material models have been developed. Nowadays, the characterization of material properties remains a top-priority objective. It requires dedicated identification methods and tests in conditions as close as possible to the real ones. This thesis aims at developing an effective identification methodology to find the material property parameters, taking advantages of all available information. The information used for the identification is theoretical, experimental, and empirical: the theoretical information is linked to the mechanical models whose uncertainty is epistemic; the experimental information consists in the full-field measurement whose uncertainty is aleatory; the empirical information is related to the prior information with epistemic uncertainty as well. The main difficulty is that the available information is not always reliable and its corresponding uncertainty is heterogeneous. This difficulty is overcome by the introduction of the theory of belief functions. By offering a general framework to represent and quantify the heterogeneous uncertainties, the performance of the identification is improved. The strategy based on the belief function is proposed to identify macro and micro elastic properties of multi-structure materials. In this strategy, model and measurement uncertainties arc analysed and quantified. This strategy is subsequently developed to take prior information into consideration and quantify its corresponding uncertainty
Mao, Yi. "Domain knowledge, uncertainty, and parameter constraints." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37295.
Full textClouse, Randy Wayne. "Evaluation of GLEAMS considering parameter uncertainty." Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/44516.
Full textClouse, Randy W. "Evaluation of GLEAMS considering parameter uncertainty /." This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-09042008-063009/.
Full textTao, Zuoyu. "Improved uncertainty estimates for geophysical parameter retrieval." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61516.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 167-169).
Algorithms for retrieval of geophysical parameters from radiances measured by instruments onboard satellites play a large role in helping scientists monitor the state of the planet. Current retrieval algorithms based on neural networks are superior in accuracy and speed compared to physics-based algorithms like iterated minimum variance (IMV). However, they do not have any form of error estimation, unlike IMV. This thesis examines the suitability of several different approaches to adding in confidence intervals and other methods of error estimation to the retrieval algorithm, as well as alternative machine learning methods that can both retrieve the parameters desired and assign error bars. Test datasets included both current generation operational instruments like AIRS/AMSU, as well as a hypothetical future hyper- spectral microwave sounder. Mixture density networks (MDN) and Sparse Pseudo Input Gaussian processes (SPGP) were found to be the most accurate at variance prediction. Both of these are novel methods in the field of remote sensing. MDNs also had similar training and testing time to neural networks, while SPGPs often took three times as long to train in typical cases. As a baseline, neural networks trained to estimate variance were also tested, but found to be lacking in accuracy and reliability compared to the other methods.
by Zuoyu Tao.
M.Eng.
Kumar, Dipmani. "Parameter uncertainty in nonpoint source pollution modeling." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-10042006-143856/.
Full textGreen, Nathan. "Optimal intervention of epidemic models with parameter uncertainty." Thesis, University of Liverpool, 2005. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:76732.
Full textHagen, David Robert. "Parameter and topology uncertainty for optimal experimental design." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90148.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 157-169).
A major effort of systems biology is the building of accurate and detailed models of biological systems. Because biological models are large, complex, and highly nonlinear, building accurate models requires large quantities of data and algorithms appropriate to translate this data into a model of the underlying system. This thesis describes the development and application of several algorithms for simulation, quantification of uncertainty, and optimal experimental design for reducing uncertainty. We applied a previously described algorithm for choosing optimal experiments for reducing parameter uncertainty as estimated by the Fisher information matrix. We found, using a computational scenario where the true parameters were unknown, that the parameters of the model could be recovered from noisy data in a small number of experiments if the experiments were chosen well. We developed a method for quickly and accurately approximating the probability distribution over a set of topologies given a particular data set. The method was based on a linearization applied at the maximum a posteriori parameters. This method was found to be about as fast as existing heuristics but much closer to the true probability distribution as computed by an expensive Monte Carlo routine. We developed a method for optimal experimental design to reduce topology uncertainty based on the linear method for topology probability. This method was a Monte Carlo method that used the linear method to quickly evaluate the topology uncertainty that would result from possible data sets of each candidate experiment. We applied the method to a model of ErbB signaling. Finally, we developed a method for reducing the size of models defined as rule-based models. Unlike existing methods, this method handles compartments of models and allows for cycles between monomers. The methods developed here generally improve the detail at which models can be built, as well as quantify how well they have been built and suggest experiments to build them even better.
by David Robert Hagen.
Ph. D.
Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.
Full textMaster of Science
Parameter uncertainty quantification seeks to determine both estimates and uncertainty regarding estimates of model parameters. Example of model parameters can include physical properties such as density, growth rates, or even deblurred images. Previous work has shown that replacing data with a surrogate model can provide promising estimates with low uncertainty. We extend the previous methods in the specific field of linear models. Theoretical results are tested on simulated computed tomography problems.
Blasone, Roberta-Serena. "Parameter estimation and uncertainty assessment in hydrological modelling." Kgs. Lyngby, 2007. http://www.er.dtu.dk/publications/fulltext/2007/MR2007-105.pdf.
Full textBooks on the topic "Parameter uncertainty"
Prat, Julien. Dynamic incentive contracts under parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2010.
Find full textFrewer, Geoff. Taxation and parameter uncertainty: Some examples. Coventry: University of Warwick,Department of Economics, 1986.
Find full textEdge, Rochelle Mary. Welfare-maximizing monetary policy under parameter uncertainty. San Francisco]: Federal Reserve Bank of San Francisco, 2007.
Find full textCateau, Gino. Monetary policy under model and data-parameter uncertainty. Ottawa: Bank of Canada, 2005.
Find full textFroot, Kenneth. The pricing of event risks with parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2001.
Find full textLarsen, Glen A. Universal currency hedging for international equity portfolios under parameter uncertainty. Bloomington, Ind: Indiana University, School of Business, 1997.
Find full textKimura, Takeshi. Optimal monetary policy in a micro-founded model with parameter uncertainty. Washington, D.C: Federal Reserve Board, 2003.
Find full textShui wen mo xing can shu gu ji fang fa ji can shu gu ji bu que ding xing yan jiu. Zhengzhou Shi: Huang He shui li chu ban she, 2010.
Find full textGiannoni, Marc Paolo. Robust optimal policy in a forward-looking model with parameter and shock uncertainty. Cambridge, Mass: National Bureau of Economic Research, 2006.
Find full textChang-Jin, Kim. Sources of monetary growth uncertainty and economic activity: The time-varying-parameter model with heteroskedasticity in the disturbance terms. [Toronto, Ont: York University, Dept. of Economics, 1990.
Find full textBook chapters on the topic "Parameter uncertainty"
Sun, Ne-Zheng, and Alexander Sun. "Model Uncertainty Quantification." In Model Calibration and Parameter Estimation, 407–58. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_10.
Full textSchaeffner, Maximilian, Christopher M. Gehb, Robert Feldmann, and Tobias Melz. "Forward vs. Bayesian Inference Parameter Calibration: Two Approaches for Non-deterministic Parameter Calibration of a Beam-Column Model." In Lecture Notes in Mechanical Engineering, 173–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_15.
Full textKlugman, Stuart A. "Prediction with Parameter Uncertainty." In Bayesian Statistics in Actuarial Science, 37–55. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-0845-6_4.
Full textSchliemann-Bullinger, Monica, Dirk Fey, Thierry Bastogne, Rolf Findeisen, Peter Scheurich, and Eric Bullinger. "The Experimental Side of Parameter Estimation." In Uncertainty in Biology, 127–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21296-8_5.
Full textMcClarren, Ryan G. "Input Parameter Distributions." In Uncertainty Quantification and Predictive Computational Science, 53–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_3.
Full textMannakee, Brian K., Aaron P. Ragsdale, Mark K. Transtrum, and Ryan N. Gutenkunst. "Sloppiness and the Geometry of Parameter Space." In Uncertainty in Biology, 271–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21296-8_11.
Full textZhang, Zhengyou, and Olivier Faugeras. "Uncertainty Manipulation and Parameter Estimation." In 3D Dynamic Scene Analysis, 9–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-58148-9_2.
Full textCedersund, Gunnar, Oscar Samuelsson, Gordon Ball, Jesper Tegnér, and David Gomez-Cabrero. "Optimization in Biology Parameter Estimation and the Associated Optimization Problem." In Uncertainty in Biology, 177–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21296-8_7.
Full textBonamente, Massimiliano. "Goodness of Fit and Parameter Uncertainty." In Statistics and Analysis of Scientific Data, 143–63. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7984-0_7.
Full textMeyer, Pierre-Jean, Alex Devonport, and Murat Arcak. "Measure of Robustness Against Parameter Uncertainty." In SpringerBriefs in Electrical and Computer Engineering, 87–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65110-7_9.
Full textConference papers on the topic "Parameter uncertainty"
Babak, O., and C. V. Deutsch. "Reserves Uncertainty Calculation Accounting for Parameter Uncertainty." In Canadian International Petroleum Conference. Petroleum Society of Canada, 2007. http://dx.doi.org/10.2118/2007-099.
Full textVidkjor, J. "S-parameter uncertainty computations." In 23rd European Microwave Conference, 1993. IEEE, 1993. http://dx.doi.org/10.1109/euma.1993.336732.
Full textDownton, Jon, and David Gray. "AVAZ parameter uncertainty estimation." In SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370006.
Full textTsai, Frank T. C. "On Prior Parameter Structure Investigation to Parameter Uncertainty." In World Water and Environmental Resources Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40792(173)378.
Full textCorlu, Canan G., Bahar Biller, and Sridhar Tayur. "Demand fulfillment probability under parameter uncertainty." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822272.
Full textDavies Ltd, K. J. "Including Parameter Uncertainty in AVA Prediction." In 64th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2002. http://dx.doi.org/10.3997/2214-4609-pdb.5.g018.
Full textDavison, Matt, Daero Kim, Harald Keller, Ilias Kotsireas, Roderick Melnik, and Brian West. "Radiotherapy Dose Fractionation under Parameter Uncertainty." In ADVANCES IN MATHEMATICAL AND COMPUTATIONAL METHODS: ADDRESSING MODERN CHALLENGES OF SCIENCE, TECHNOLOGY, AND SOCIETY. AIP, 2011. http://dx.doi.org/10.1063/1.3663489.
Full textStenarson, J., and K. Yhland. "Uncertainty propagation through network parameter conversions." In 2008 Conference on Precision Electromagnetic Measurements (CPEM 2008). IEEE, 2008. http://dx.doi.org/10.1109/cpem.2008.4574836.
Full textRanda, James. "Uncertainty analysis for noise-parameter measurements." In 2008 Conference on Precision Electromagnetic Measurements (CPEM 2008). IEEE, 2008. http://dx.doi.org/10.1109/cpem.2008.4574871.
Full textWu, Guangbin, Guoqiang Liang, and Junwei Lei. "Research on parameters identification of system with uncertainty and unknown parameter." In 2016 4th International Conference on Machinery, Materials and Computing Technology. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icmmct-16.2016.389.
Full textReports on the topic "Parameter uncertainty"
Hardin, Ernest, Teklu Hadgu, Harris Greenberg, and Mark Dupont. Parameter Uncertainty for Repository Thermal Analysis. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1331495.
Full textPrat, Julien, and Boyan Jovanovic. Dynamic Incentive Contracts Under Parameter Uncertainty. Cambridge, MA: National Bureau of Economic Research, December 2010. http://dx.doi.org/10.3386/w16649.
Full textBanks, H. T., and Kathleen L. Bihari. Modeling and Estimating Uncertainty in Parameter Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada447550.
Full textRanda, James. Uncertainty analysis for NIST noise-parameter measurements. Gaithersburg, MD: National Bureau of Standards, 2008. http://dx.doi.org/10.6028/nist.tn.1530.
Full textFroot, Kenneth, and Steven Posner. The Pricing of Event Risks with Parameter Uncertainty. Cambridge, MA: National Bureau of Economic Research, February 2001. http://dx.doi.org/10.3386/w8106.
Full textYang, David Y. Incorporating Model Parameter Uncertainty into Prostate IMRT Treatment Planning. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada439169.
Full textMeyer, Philip D., Ming Ye, Shlomo P. Neuman, and Kirk J. Cantrell. Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/974518.
Full textPlaskett, Joseph. Parameter uncertainty and modeling of sludge dewatering in one dimension. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6316.
Full textHund, Lauren, and Justin Brown. Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562417.
Full textSingh, D., M. Salter, J. Skinner, and N. M. Ridler. Commissioning of a VNA dynamic uncertainty tool for microwave S-parameter measurements. National Physical Laboratory, February 2021. http://dx.doi.org/10.47120/npl.tqe16.
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