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Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter Estimation. [PDF]

CPT Pharmacometrics Syst Pharmacol
Teutonico D, Marchionni D, Lavielle M, Nguyen L.   +3 more
europepmc   +1 more source

Evaluating Pancreatic Cancer Treatment Strategies Using a Novel Polytopic Fuzzy Tensor Approach. [PDF]

Bioengineering (Basel)
Bilal M, Li C, Alzahrani AK, Aljahdali AK.   +3 more
europepmc   +1 more source

Model Uncertainty, State Uncertainty, and State-Space Models [PDF]

SSRN Electronic Journal, 2012
State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables.
Young, ER, Luo, Y, Nie, J
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MODEL UNCERTAINTY

Journal of the European Economic Association, 2015
We study decision problems in which consequences of the various alternative actions depend on states determined by a generative mechanism representing some natural or social phenomenon. Model uncertainty arises because decision makers may not know this mechanism. Two types of uncertainty result, a state uncertainty within models and a model uncertainty
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Model Uncertainty

Mental models help people navigate complex environments. This paper studies how people deal with model uncertainty. In an experiment, participants estimate a company's value, facing uncertainty about which one of two models correctly deter- mines its true value. Using a between-subjects design, we vary the degree of model complexity.
Musolff, R., Zimmermann, F.
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A Fresh Perspective on Uncertainty Modeling: Uncertainty Vs. Uncertainty Modeling

1998
It is argued that very often when talking about the uncertainty of a system people confuse the phenomena with the glasses (theories) which they use to observe or model the uncertain phenomenon. Some experts also claim, that there is only one valid theory or tool (f. i. probability theory) to model all kinds of uncertainty. In this paper it is suggested,
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MODEL UNCERTAINTY AND SCENARIO AGGREGATION

Mathematical Finance, 2014
This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the definition of five fundamental criteria that serve as a basis for our method.
Cambou, Mathieu, Filipović, Damir
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Model Uncertainty

2019
Abstract This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
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Uncertainty in Climate Models

Science, 2002
Thomas M. Smith et al. (“how accurate are climate simulations?”, Perspectives, 19 April, p. [483][1]) suggest that today's climate models simulate the climate history of Earth over the past 150 years “within the observed uncertainty of the observations.” In comparing model results with trends in sea surface temperature in several ocean basins, they ...
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