Bibliografie tematiche / Parameter uncertainty

Letteratura scientifica selezionata sul tema "Parameter uncertainty"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 31 gennaio 2023

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Articoli di riviste sul tema "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, n. 6 (31 dicembre 2020): 445–50. http://dx.doi.org/10.9798/kosham.2020.20.6.445.

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This study quantitatively analyzed the degree of uncertainty associated with runoff based on the sensitivity analysis of runoff parameters using Low Impact Development (LID) element technology of study (I). Uncertainty was analyzed for parameter uncertainty, uncertainty of runoff, and uncertainty about the degree of parameter and runoff. Parameter uncertainty indices showed lower uncertainty indices as a whole and uncertainty indices of peak runoff were higher than that of total runoff in runoff uncertainty. The reason for this is that the LID element technology itself is intended to store low-frequency small-scale rainfall, so that the uncertainty index of peak rainfall seems to be highly uncertain. As a result of the analysis of uncertainty degree associated with runoff, it was found that the uncertainty of storage depth of bio retention cell and rain garden was low, while the heaviness parameters of rain barrel had the highest uncertainty index. In future experiments and research, it is necessary to modify the parameter range suitable for Korea, which will be helpful for urban development, reduction of nonpoint source pollution, and designing of low frequency rainfall storage facilities.

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Højberg, A. L., e J. C. Refsgaard. "Model uncertainty – parameter uncertainty versus conceptual models". Water Science and Technology 52, n. 6 (1 settembre 2005): 177–86. http://dx.doi.org/10.2166/wst.2005.0166.

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Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.

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Chen, Si, Guoqi Xie, Renfa Li e Keqin Li. "Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis". ACM Transactions on Design Automation of Electronic Systems 27, n. 3 (31 maggio 2022): 1–19. http://dx.doi.org/10.1145/3490177.

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Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

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Weise, K. "Uncertainty of Parameter Estimation". IFAC Proceedings Volumes 18, n. 5 (luglio 1985): 1717–22. http://dx.doi.org/10.1016/s1474-6670(17)60816-4.

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Paulter, N. G., e D. R. Larson. "Pulse parameter uncertainty analysis". Metrologia 39, n. 2 (aprile 2002): 143–55. http://dx.doi.org/10.1088/0026-1394/39/2/4.

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Wakeland, Wayne, e Jack Homer. "Addressing Parameter Uncertainty in a Health Policy Simulation Model Using Monte Carlo Sensitivity Methods". Systems 10, n. 6 (18 novembre 2022): 225. http://dx.doi.org/10.3390/systems10060225.

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We present a practical guide and step-by-step flowchart for establishing uncertainty in-tervals for key model outcomes in a simulation model in the face of uncertain parameters. The process start with Powell optimization to find a set of uncertain parameters (the optimum param-eter set or OPS) that minimize the model fitness error relative to historical data. Optimization also help in refinement of parameter uncertainty ranges. Next, traditional Monte Carlo (TMC) ran-domization or Markov Chain Monte Carlo (MCMC) is used to create a sample of parameter sets that fit the reference behavior data nearly as well as the OPS. Under the TMC method, the entire pa-rameter space is explored broadly with a large number of runs, and the results are sorted for se-lection of qualifying parameter sets (QPS) to ensure good fit and parameter distributions that are centrally located within the uncertainty ranges. In addition, the QPS outputs are graphed as sen-sitivity graphs or box-and-whisker plots for comparison with the historical data. Finally, alternative policies and scenarios are run against the OPS and all QPS, and uncertainty intervals are found for projected model outcomes. We illustrate the full parameter uncertainty approach with a (previ-ously published) system dynamics model of the U.S. opioid epidemic, and demonstrate how it can enrich policy modeling results.

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Bai, Jie, Shuai Liu e Wei Wang. "Study on Identification Method for Parameter Uncertainty Model of Aero Engine". International Journal of Aerospace Engineering 2019 (2 dicembre 2019): 1–9. http://dx.doi.org/10.1155/2019/6015270.

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The linear model of an aero engine is effective in a small range of the neighborhood of equilibrium points. According to this problem, the identification method for the parameter uncertain linear model of the aero engine was proposed. The identification problem is solved by calculating nonlinear programming. Considering the parameter uncertainty of the model is the critical point of this research during the optimization process. A parameter uncertain model of an aero engine can be obtained, which has large use range. This method is used for DGEN380 aero engine. The two parameters, VDD and VE, are defined for describing error range. Compared with experimental data, the uncertain model of DGEN 380 can simulate the real state of DGEN380 within 1% error range when ΔPLA<22%. Compared with another conventional method of identification (recursive least squares), the parameter uncertain model, established by the method of this research, has a broad application area through parameter uncertainty of the model.

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Mousavi, S. Jamshid, K. C. Abbaspour, B. Kamali, M. Amini e H. Yang. "Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach". Journal of Hydroinformatics 14, n. 2 (10 maggio 2011): 286–309. http://dx.doi.org/10.2166/hydro.2011.071.

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This study presents the application of an uncertainty-based technique for automatic calibration of the well-known Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) model. Sequential uncertainty fitting (SUFI2) approach has been used in calibration of the HEC-HMS model built for Tamar basin located in north of Iran. The basin was divided into seven sub-basins and three routing reaches with 24 parameters to be estimated. From the four events, three were used for calibration and one for verification. Each event was initially calibrated separately. As there was no unique parameter set identified, all events were then calibrated jointly. Based on the scenarios of separately and jointly calibrated events, different candidate parameter sets were inputted to the model verification stage where recalibration of initial abstraction parameters commenced. Some of the candidate parameter sets with no physically meaningful parameter values were withdrawn after recalibration. Then new ranges of parameters were identified based on minimum and maximum values of the remaining parameter sets. The new parameter ranges were used in an uncertainty analysis using SUFI2 technique resulting in much narrower parameter intervals that can simulate both verification and calibration events satisfactorily in a probabilistic sense. Results show that the SUFI2 technique linked to HEC-HMS as a simulation–optimization model can provide a basis for performing uncertainty-based automatic calibration of event-based hydrologic models.

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Pernot, Pascal. "The parameter uncertainty inflation fallacy". Journal of Chemical Physics 147, n. 10 (14 settembre 2017): 104102. http://dx.doi.org/10.1063/1.4994654.

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Gerrard, R., e A. Tsanakas. "Failure Probability Under Parameter Uncertainty". Risk Analysis 31, n. 5 (22 dicembre 2010): 727–44. http://dx.doi.org/10.1111/j.1539-6924.2010.01549.x.

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Più fonti

Tesi sul tema "Parameter uncertainty"

Sui, Liqi. "Uncertainty management in parameter identification". Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.

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Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisée pour l'identification est à la fois théorique, expérimentale et empirique : l'information théorique est liée aux modèles mécaniques dont l'incertitude est épistémique; l'information expérimentale provient ici de la mesure de champs cinématiques obtenues pendant l'essai ct dont l'incertitude est aléatoire; l'information empirique est liée à l'information à priori associée à une incertitude épistémique ainsi. La difficulté principale est que l'information disponible n'est pas toujours fiable et que les incertitudes correspondantes sont hétérogènes. Cette difficulté est surmontée par l'utilisation de la théorie des fonctions de croyance. En offrant un cadre général pour représenter et quantifier les incertitudes hétérogènes, la performance de l'identification est améliorée. Une stratégie basée sur la théorie des fonctions de croyance est proposée pour identifier les propriétés élastiques macro et micro des matériaux multi-structures. Dans cette stratégie, les incertitudes liées aux modèles et aux mesures sont analysées et quantifiées. Cette stratégie est ensuite étendue pour prendre en compte l'information à priori et quantifier l'incertitude associée
In 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

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Mao, Yi. "Domain knowledge, uncertainty, and parameter constraints". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37295.

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Clouse, Randy Wayne. "Evaluation of GLEAMS considering parameter uncertainty". Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/44516.

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Clouse, Randy W. "Evaluation of GLEAMS considering parameter uncertainty /". This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-09042008-063009/.

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Tao, Zuoyu. "Improved uncertainty estimates for geophysical parameter retrieval". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61516.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
This 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.

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Kumar, Dipmani. "Parameter uncertainty in nonpoint source pollution modeling". Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-10042006-143856/.

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Green, 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.

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Hagen, David Robert. "Parameter and topology uncertainty for optimal experimental design". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90148.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014.
Cataloged 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.

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Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes". Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.

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We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.
Master 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.

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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.

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Libri sul tema "Parameter uncertainty"

Prat, Julien. Dynamic incentive contracts under parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2010.

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Frewer, Geoff. Taxation and parameter uncertainty: Some examples. Coventry: University of Warwick,Department of Economics, 1986.

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Edge, Rochelle Mary. Welfare-maximizing monetary policy under parameter uncertainty. San Francisco]: Federal Reserve Bank of San Francisco, 2007.

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Cateau, Gino. Monetary policy under model and data-parameter uncertainty. Ottawa: Bank of Canada, 2005.

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Froot, Kenneth. The pricing of event risks with parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2001.

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Larsen, Glen A. Universal currency hedging for international equity portfolios under parameter uncertainty. Bloomington, Ind: Indiana University, School of Business, 1997.

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Kimura, Takeshi. Optimal monetary policy in a micro-founded model with parameter uncertainty. Washington, D.C: Federal Reserve Board, 2003.

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Shui 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.

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Giannoni, Marc Paolo. Robust optimal policy in a forward-looking model with parameter and shock uncertainty. Cambridge, Mass: National Bureau of Economic Research, 2006.

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Chang-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.

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Capitoli di libri sul tema "Parameter uncertainty"

Sun, Ne-Zheng, e 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.

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Schaeffner, Maximilian, Christopher M. Gehb, Robert Feldmann e 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.

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AbstractMathematical models are commonly used to predict the dynamic behavior of mechanical structures or to synthesize controllers for active systems. Calibrating the model parameters to experimental data is crucial to achieve reliable and adequate model predictions. However, the experimental dynamic behavior is uncertain due to variations in component properties, assembly and mounting. Therefore, uncertainty in the model parameters can be considered in a non-deterministic calibration. In this paper, we compare two approaches for a non-deterministic parameter calibration, which both consider uncertainty in the parameters of a beam-column model. The goal is to improve the model prediction of the axial load-dependent lateral dynamic behavior. The investigation is based on a beam-column system subjected to compressive axial loads used for active buckling control. A representative sample of 30 nominally identical beam-column systems characterizes the variations in the experimental lateral axial load-dependent dynamic behavior. First, in a forward parameter calibration approach, the parameters of the beam-column model are calibrated separately for all 30 investigated beam-column systems using a least squares optimization. The uncertainty in the parameters is obtained by assuming normal distributions of the separately calibrated parameters. Second, in a Bayesian inference parameter calibration approach, the parameters are calibrated using the complete sample of experimental data. Posterior distributions of the parameters characterize the uncertain dynamic behavior of the beam-column model. For both non-deterministic parameter calibration approaches, the predicted uncertainty ranges of the axial load-dependent lateral dynamic behavior are compared to the uncertain experimental behavior and the most accurate results are identified.

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Klugman, 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.

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Schliemann-Bullinger, Monica, Dirk Fey, Thierry Bastogne, Rolf Findeisen, Peter Scheurich e 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.

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McClarren, 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.

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Mannakee, Brian K., Aaron P. Ragsdale, Mark K. Transtrum e 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.

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Zhang, Zhengyou, e 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.

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Cedersund, Gunnar, Oscar Samuelsson, Gordon Ball, Jesper Tegnér e 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.

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Bonamente, 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.

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Meyer, Pierre-Jean, Alex Devonport e 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.

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Atti di convegni sul tema "Parameter uncertainty"

Babak, O., e 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.

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Vidkjor, J. "S-parameter uncertainty computations". In 23rd European Microwave Conference, 1993. IEEE, 1993. http://dx.doi.org/10.1109/euma.1993.336732.

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Downton, Jon, e 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.

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Tsai, 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.

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Corlu, Canan G., Bahar Biller e 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.

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Davies 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.

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Davison, Matt, Daero Kim, Harald Keller, Ilias Kotsireas, Roderick Melnik e 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.

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Stenarson, J., e 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.

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Randa, 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.

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Wu, Guangbin, Guoqiang Liang e 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.

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Rapporti di organizzazioni sul tema "Parameter uncertainty"

Hardin, Ernest, Teklu Hadgu, Harris Greenberg e Mark Dupont. Parameter Uncertainty for Repository Thermal Analysis. Office of Scientific and Technical Information (OSTI), ottobre 2015. http://dx.doi.org/10.2172/1331495.

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Prat, Julien, e Boyan Jovanovic. Dynamic Incentive Contracts Under Parameter Uncertainty. Cambridge, MA: National Bureau of Economic Research, dicembre 2010. http://dx.doi.org/10.3386/w16649.

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Banks, H. T., e Kathleen L. Bihari. Modeling and Estimating Uncertainty in Parameter Estimation. Fort Belvoir, VA: Defense Technical Information Center, gennaio 1999. http://dx.doi.org/10.21236/ada447550.

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Randa, James. Uncertainty analysis for NIST noise-parameter measurements. Gaithersburg, MD: National Bureau of Standards, 2008. http://dx.doi.org/10.6028/nist.tn.1530.

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Froot, Kenneth, e Steven Posner. The Pricing of Event Risks with Parameter Uncertainty. Cambridge, MA: National Bureau of Economic Research, febbraio 2001. http://dx.doi.org/10.3386/w8106.

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Yang, David Y. Incorporating Model Parameter Uncertainty into Prostate IMRT Treatment Planning. Fort Belvoir, VA: Defense Technical Information Center, aprile 2005. http://dx.doi.org/10.21236/ada439169.

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Meyer, Philip D., Ming Ye, Shlomo P. Neuman e Kirk J. Cantrell. Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty. Office of Scientific and Technical Information (OSTI), marzo 2004. http://dx.doi.org/10.2172/974518.

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Plaskett, Joseph. Parameter uncertainty and modeling of sludge dewatering in one dimension. Portland State University Library, gennaio 2000. http://dx.doi.org/10.15760/etd.6316.

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Hund, Lauren, e Justin Brown. Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output. Office of Scientific and Technical Information (OSTI), settembre 2016. http://dx.doi.org/10.2172/1562417.

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Singh, D., M. Salter, J. Skinner e N. M. Ridler. Commissioning of a VNA dynamic uncertainty tool for microwave S-parameter measurements. National Physical Laboratory, febbraio 2021. http://dx.doi.org/10.47120/npl.tqe16.

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