Web: http://arxiv.org/abs/2206.11343

June 24, 2022, 1:11 a.m. | Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden

stat.ML updates on arXiv.org arxiv.org

We consider the Bayesian calibration of models describing the phenomenon of
block copolymer (BCP) self-assembly using image data produced by microscopy or
X-ray scattering techniques. To account for the random long-range disorder in
BCP equilibrium structures, we introduce auxiliary variables to represent this
aleatory uncertainty. These variables, however, result in an integrated
likelihood for high-dimensional image data that is generally intractable to
evaluate. We tackle this challenging Bayesian inference problem using a
likelihood-free approach based on measure transport together with …

arxiv bayesian computation free inference information model physics transport

More from arxiv.org / stat.ML updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY