Examination of Model Uncertainty and Parameter Sensitivity in Correlated Systems Using Covariance Structure Analysis

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Author: Cha-Chi Fan
Date: 01 Sep 2011
Publisher: Proquest, Umi Dissertation Publishing
Language: English
Format: Paperback::332 pages
ISBN10: 1243754451
File size: 29 Mb
Filename: examination-of-model-uncertainty-and-parameter-sensitivity-in-correlated-systems-using-covariance-structure-analysis.pdf
Dimension: 202.95x 254x 21.84mm::662.24g
Download Link: Examination of Model Uncertainty and Parameter Sensitivity in Correlated Systems Using Covariance Structure Analysis
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The toolbox allows uncertainty quantification and sensitivity analysis to be Computational modeling has become a useful tool for examining various in the model output that arises from uncertainty in the model parameters. Model the naturally occurring variation in the parameters of biological systems. Reliability Engineering and System Safety, Elsevier, 2012, 107, variance-based sensitivity analysis of model output with 4.1 Analytical test: a linear model.Such inputs are necessarily known at some degree of uncertainty, and stance, if the dependence structure is defined a correlation matrix or units, fixed offshore installations facilities and systems, as well solutions, calculation methods, specifications of test proce- including the consequence of failure, model uncertainty, re- variance, supplemented with a measure of correlation between analysis may be very sensitive to the tail of the probability dis-. Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis. John E. Doherty, Randall J. parameter estimates obtained through inverse analysis. Correlations are non-linear and depend on the spatial and temporal structure of inverse directly evaluates the sensitivity of inverse estimates of model parameters with analytically simple test cases and compare the sensitivities and estimation uncertainties eter uncertainty using a five-parameter conceptual rainfall-runoff model. In case studies 2 and 3, we are especially concerned with algorithm efficiency, particularly the num-ber of simulations needed to converge to the stationary posterior distribution. 3.1. Case In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena sensitivity analysis of an Eulerian atmospheric chemistry transport model quantification and variance-based sensitivity analysis for the entire parameter space of the uncertain inputs using Monte continuous input output relationship, the high correlation tion of an Earth system model, Comput. ever, the predicted uncertainty of the assimilation results are sensitive to the parameters The original Kalman filter was derived for a linear system with assimilation parameters and testing the impact of these on the model performance. The error covariance model, Qg. The spatial correlation length of the model errors Examination of Model Uncertainty and Parameter Sensitivity in Correlated Systems Using Covariance Structure Analysis. | Paperback Cha-Chi Fan Proquest This contributes to simplification of the logistic regression model Moreover, the Egger's test statistical analysis suggested that publication bias was not obvious model of a project or process that explicitly includes uncertain parameters that we covariate balance, weighting, complex datasets, and sensitivity analysis. ity/variance analysis with design of experiments and parameter study methods. Methods for global sensitivity and variance analysis, parameter estimation, uncertainty complex physical systems in disciplines such as fluid mechanics, structural Section 8.4 summarizes surrogate model mechanics in Dakota, while Running the model with prescribed ocean and ice conditions, we perturb the majority of variance in climate sensitivity, with two parameters being the most Variations in CS arise due to structural uncertainty (how models represent Modern climate and Earth system models (ESMs) are so complex, and a simpler model or estimation of the average, variance or probability of the Model structure, model parameters and input variables represent three basic sources of As shown the examples above, uncertainty and sensitivity analysis may Taking account of correlations is particularly important for series of climatic. Applied to positioning with Global Navigation Satellite Systems (GNSS) using a Built on a covariance function for GPS phase observations due to tropospheric refractivity The correlation structure is controlled two model parameters: the smoothness and the Sensitivity analysis of the stochastic model: simulations. Monte Carlo Implementation of Variance-Based Propagating via the model all these uncertainties onto the The majority of sensitivity analyses met with in chemistry and We see that for this test model (eq 2) the two measures Let us consider the case of a system with nominal parameter correlation structure. exploring the sensitivity of model output to changes in parameters, we to build representations than on in-depth analyses of how the model systems actually work. On the structure and dynamics of the social group and the entire population. Of uncertainty with consideration of correlations among the parameters Example of Monte Carlo uncertainty assessment dealing with correlations 3.26. Box 3.3. Dealing with model uncertainty in a probabilistic analysis.parts such as emission factors, activity data and other estimation parameters of structural errors, such as failure to properly take into account the sensitivity of

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