30 research outputs found
Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA
It has become commonplace to use complex computer models to predict outcomes
in regions where data does not exist. Typically these models need to be
calibrated and validated using some experimental data, which often consists of
multiple correlated outcomes. In addition, some of the model parameters may be
categorical in nature, such as a pointer variable to alternate models (or
submodels) for some of the physics of the system. Here we present a general
approach for calibration in such situations where an emulator of the
computationally demanding models and a discrepancy term from the model to
reality are represented within a Bayesian Smoothing Spline (BSS) ANOVA
framework. The BSS-ANOVA framework has several advantages over the traditional
Gaussian Process, including ease of handling categorical inputs and correlated
outputs, and improved computational efficiency. Finally this framework is then
applied to the problem that motivated its design; a calibration of a
computational fluid dynamics model of a bubbling fluidized which is used as an
absorber in a CO2 capture system
A Frequentist Approach to Computer Model Calibration
This paper considers the computer model calibration problem and provides a
general frequentist solution. Under the proposed framework, the data model is
semi-parametric with a nonparametric discrepancy function which accounts for
any discrepancy between the physical reality and the computer model. In an
attempt to solve a fundamentally important (but often ignored) identifiability
issue between the computer model parameters and the discrepancy function, this
paper proposes a new and identifiable parametrization of the calibration
problem. It also develops a two-step procedure for estimating all the relevant
quantities under the new parameterization. This estimation procedure is shown
to enjoy excellent rates of convergence and can be straightforwardly
implemented with existing software. For uncertainty quantification,
bootstrapping is adopted to construct confidence regions for the quantities of
interest. The practical performance of the proposed methodology is illustrated
through simulation examples and an application to a computational fluid
dynamics model.Comment: 21 pages, 2 figure
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A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.
Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost
Measuring the impact of ambulatory red blood cell transfusion on home functional status: study protocol for a pilot randomized controlled trial
SPIRIT 2013: SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist for clinical trial protocols. (DOCX 65 kb