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Showing 1 to 7 of 7 for “"Overparameterization"”.
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Toward improved evaluation of large scale hydrologic models: estimation and quantification of parameter uncertainty
… quantification methods were addressed: overparameterization and reduction of parameter uncertainty through quantitative information. Parameters were categorized as distributed, inactive, or lumped by combining traditional concepts from identifiability and overparameterization with …
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Improvements in Adaptive IIR Filtering: Theory and Application
… of the error surface. The adverse effect of overparameterization which can have serious practical implications is shown through an example. Also, it is shown that for certain insufficient order filters, a nonminimum phase characteristic is sufficient for multimodality of the error surface …
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Application of Hidden Markov Model in Finite Mixture Modeling of High-Dimensional Data
… storagecapabilities, well-known issues such as overparameterization may emerge in the FMM framework, which leads to underestimation of the correct mixture order. This issue has motivatedthe current dissertation, as a part of which the FMM framework that can be applied todata with higher …
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Volatility Modeling Using the Student's t Distribution
… models, which in addition have the problem of overparameterization. This dissertation uses the Student's t distribution and follows the Probabilistic Reduction (PR) methodology to modify and extend the univariate and multivariate volatility models viewed as alternative to the GARCH models. Its …
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Algorithms and Algorithmic Barriers in High-Dimensional Statistics and Random Combinatorial Structures
… many data. Our results explain why the overparameterization does not hurt the generalization ability for such architectures. This conundrum has been observed empirically in NNs and defies the classical statistical wisdom. • Our final focus is on the problem of learning two-layer NNs with …
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Operator learning in the overparameterized regime
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Blood-Oxygen-Level-Dependent Parameter Identification using Multimodal Neuroimaging and Particle Filters
The Blood Oxygen Level Dependent (BOLD) signal provides indirect estimates of neural activity. The parameters of this BOLD signal can give information about the pathophysiological state of the brain. Most of the models for the BOLD signal are overparameterized which makes the unique identification …