|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Computational Methods and Experimental Approaches for Uncertainty Quantification and Propagation, Model Validation, and Stochastic Predictions
||Information-theoretic Tools for Uncertainty Quantification of High Dimensional Stochastic Models
||Petr Plechac, Ting Wang
|On-Site Speaker (Planned)
We present mathematical tools for deriving optimal, computable bounds on local and global sensitivity indices of observables for complex stochastic models arising in biology, reaction kinetics and materials science. The presented technique allows for deriving bounds also for path-dependent functionals and risk sensitive functionals. We discuss problems and solutions to sensitivity estimation in stochastic systems with multiple disparate time scales. The use of variational representation of relative entropy also allows for error estimation, and uncertainty quantification in coarse-grained models.