The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto parameters. The signal-to-noise ratio of error covariance between model states and initially perturbed parameters determinates directly the success of parameter...

In the BEPU (Best Estimate Plus Uncertainty) framework, uncertainty quantification (UQ) is a requirement to improve confidence and reliability of code predictions. Over years, lot works have been done quantify uncertainties in predictions spent nuclear fuel (SNF) characteristics due data uncertainties. The purpose this study pressurized water reactor (PWR) assembly radiation source terms (isoto...

In this paper, we use Boltzmann statistics and the maximum likelihood distribution derived from Bayes’ Theorem to infer parameter values for a Pake Doublet Spectrum, a lineshape of historical significance and contemporary relevance for determining distances between interacting magnetic dipoles. A Metropolis Hastings Markov Chain Monte Carlo algorithm is implemented and designed to find the opti...

Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the syste...

The strengths of future carbon dioxide (CO 2) sinks are highly uncertain. A sound characterization of this uncertainty and how they might be reduced is crucial for the design of efficient carbon management strategies. Here a Bayesian synthesis inversion of historical carbon cycle observations is used to (i) estimate probability density functions (PDFs) of key carbon cycle parameters, (ii) deriv...