Department seminar by Oksana Chkrebtii, The Ohio State UniversityExport this event to calendar

Thursday, March 29, 2018 — 4:00 PM EDT

Probability models for discretization uncertainty with adaptive grid designs for systems of differential equations


When models are defined implicitly by systems of differential equations without a closed form solution, small local errors in finite-dimensional solution approximations can propagate into large deviations from the true underlying state trajectory. Inference for such models relies on a likelihood approximation constructed around a numerical solution, which underestimates posterior uncertainty. This talk will introduce and discuss progress in a new adaptive formalism for modeling and propagating discretization uncertainty through the Bayesian inferential framework, allowing exact inference and uncertainty quantification for discretized differential equation models.  

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West
Waterloo, ON N2L 3G1
Canada

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