Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen
OPUS4-6632 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J.; Teckentrup, Aretha Random forward models and log-likelihoods in Bayesian inverse problems We consider the use of randomised forward models and log-likelihoods within the Bayesian approach to inverse problems. Such random approximations to the exact forward model or log-likelihood arise naturally when a computationally expensive model is approximated using a cheaper stochastic surrogate, as in Gaussian process emulation (kriging), or in the field of probabilistic numerical methods. We show that the Hellinger distance between the exact and approximate Bayesian posteriors is bounded by moments of the difference between the true and approximate log-likelihoods. Example applications of these stability results are given for randomised misfit models in large data applications and the probabilistic solution of ordinary differential equations. 29 SIAM/ASA Journal on Uncertainty Quantification 6 4 1600 1629 urn:nbn:de:0297-zib-66324 10.1137/18M1166523 Numerical Mathematics
OPUS4-6241 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J. Quasi-invariance of countable products of Cauchy measures under non-unitary dilations 2018 6 Electronic Communications in Probability 23 8 1 6 10.1214/18-ECP113 Numerical Mathematics
OPUS4-6934 Konferenzveröffentlichung Teymur, Onur; Lie, Han Cheng; Sullivan, T. J.; Calderhead, Ben Implicit probabilistic integrators for ODEs Advances in Neural Information Processing Systems 31 (NIPS 2018) Numerical Mathematics
OPUS4-7105 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J. Erratum: Equivalence of weak and strong modes of measures on topological vector spaces (2018 Inverse Problems 34 115013) Inverse Problems 34 12 129601 10.1088/1361-6420/aae55b Numerical Mathematics
OPUS4-6660 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J. Equivalence of weak and strong modes of measures on topological vector spaces Modes of a probability measure on an infinite-dimensional Banach space X are often defined by maximising the small-radius limit of the ratio of measures of norm balls. Helin and Burger weakened the definition of such modes by considering only balls with centres in proper subspaces of X, and posed the question of when this restricted notion coincides with the unrestricted one. We generalise these definitions to modes of arbitrary measures on topological vector spaces, defined by arbitrary bounded, convex, neighbourhoods of the origin. We show that a coincident limiting ratios condition is a necessary and sufficient condition for the equivalence of these two types of modes, and show that the coincident limiting ratios condition is satisfied in a wide range of topological vector spaces. Inverse Problems 34 11 115013 10.1088/1361-6420/aadef2 Numerical Mathematics
OPUS4-7146 Wissenschaftlicher Artikel Kersting, Hans; Sullivan, T. J.; Hennig, Philipp Convergence rates of Gaussian ODE filters Numerical Mathematics
OPUS4-6975 misc Nava-Yazdani, Esfandiar; Hege, Hans-Christian; von Tycowicz, Christoph; Sullivan, T. J. A Shape Trajectories Approach to Longitudinal Statistical Analysis For Kendall's shape space we determine analytically Jacobi fields and parallel transport, and compute geodesic regression. Using the derived expressions, we can fully leverage the geometry via Riemannian optimization and reduce the computational expense by several orders of magnitude. The methodology is demonstrated by performing a longitudinal statistical analysis of epidemiological shape data. As application example we have chosen 3D shapes of knee bones, reconstructed from image data of the Osteoarthritis Initiative. Comparing subject groups with incident and developing osteoarthritis versus normal controls, we find clear differences in the temporal development of femur shapes. This paves the way for early prediction of incident knee osteoarthritis, using geometry data only. urn:nbn:de:0297-zib-69759 Visual Data Analysis