COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Engineering - Dynamics and Vibration Tea Time Talks > Challenges in Bayesian inference and reliability with large numbers of uncertain parameters
Challenges in Bayesian inference and reliability with large numbers of uncertain parametersAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact div-c. Efficient sampling methods play a central role in Bayesian inference, reliability analysis and machine learning. Practical applications in structural and geotechnical engineering often deal with high-dimensional parameter spaces and computationally expensive physics-based models, rendering many sampling-based approaches infeasible. In this talk we initially examine these challenges through the case studies of a steel girder road bridge and a sheet pile quay wall. Motivated by recent work on machine learning approaches for nested sampling, we explore the connection between nested sampling and importance sampling, subset simulation and normalizing flows, in an effort to derive an optimization-based approach for inference and reliability estimation that takes advantage of the key ideas behind nested sampling. This talk is part of the Engineering - Dynamics and Vibration Tea Time Talks series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsType the title of a new list here Rouse Ball Lectures Saffron Hall EventsOther talksKnowledge Transfer in the Rich and Nonlinear Tomography Programme Retarded motile active matter The Innovate UK Analysis for Innovators Programme. A Case Study: Improving the Accuracy of the Friction Flowmeter Facing the MUSIC: towards a robust and flexible research code for stellar hydrodynamics Resource Efficiency: delivering future energy and material services with less environmental impact. |