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Thursday, May 17
Bayesian Modeling
Thu, May 17, 3:00 PM - 3:45 PM
Regency Ballroom B
 

On the Quantification and Efficient Propagation of Imprecise Probabilities Using Monte Carlo Methods (304712)

*Jiaxin Zhang, Johns Hopkins University  

Keywords: Uncertainty quantification, Uncertainty propagation, Bayesian, Monte Carlo simulation, Imprecise probability

This work addresses the challenge of uncertainty quantification (UQ) and propagation when data for characterizing probability model is limited. We propose a Bayesian multimodel UQ methodology wherein the full uncertainty associated with probability model form and parameter estimation are retained and efficiently propagated. The result shows a complete probabilistic description of both aleatory and epistemic uncertainty achieved with several orders of magnitude reduction in computational cost. As additional data are collected, the probability measure inferred from Bayesian inference may change significantly. In such cases, it is undesirable to perform a new Monte Carlo analysis using the updated density as it results in large added computational costs. In this work, we proposed a mixed augmenting-filtering resampling algorithm that can efficiently accommodate a measure change in Monte Carlo simulation that minimizes the impact on the sample set and saves a large amount of additional computational cost. In addition, we present an investigation into the effect of prior probabilities on the resulting uncertainties. It is illustrated that prior probabilities can have a significant impact on multimodel UQ for small datasets and inappropriate (but seemingly reasonable) priors may even have lingering effects that bias probabilities even for large datasets.