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Challenges in Bayesian inference and reliability with large numbers of uncertain parameters

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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.

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LR5, Cambridge University Engineering Department Thomas Lecture theatre, Sanger Building, Tennis Court Road Drum Building, Madingley Rise site, West Cambridge and on Zoom: https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09

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