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
OPUS4-7144 Wissenschaftlicher Artikel Oates, Chris. J.; Sullivan, T.J. A modern retrospective on probabilistic numerics This article attempts to place the emergence of probabilistic numerics as a mathematical-statistical research field within its historical context and to explore how its gradual development can be related to modern formal treatments and applications. We highlight in particular the parallel contributions of Sul'din and Larkin in the 1960s and how their pioneering early ideas have reached a degree of maturity in the intervening period, mediated by paradigms such as average-case analysis and information-based complexity. We provide a subjective assessment of the state of research in probabilistic numerics and highlight some difficulties to be addressed by future works. Statistics and Computing Numerical Mathematics
OPUS4-7145 Wissenschaftlicher Artikel Oates, Chris. J.; Cockayne, Jon; Prangle, Dennis; Sullivan, T. J.; Girolami, Mark Optimality criteria for probabilistic numerical methods It is well understood that Bayesian decision theory and average case analysis are essentially identical. However, if one is interested in performing uncertainty quantification for a numerical task, it can be argued that the decision-theoretic framework is neither appropriate nor sufficient. To this end, we consider an alternative optimality criterion from Bayesian experimental design and study its implied optimal information in the numerical context. This information is demonstrated to differ, in general, from the information that would be used in an average-case-optimal numerical method. The explicit connection to Bayesian experimental design suggests several distinct regimes in which optimal probabilistic numerical methods can be developed. Numerical Mathematics
OPUS4-6014 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J. Cameron--Martin theorems for sequences of Cauchy-distributed random variables Given a sequence of Cauchy-distributed random variables defined by a sequence of location parameters and a sequence of scale parameters, we consider another sequence of random variables that is obtained by perturbing the location or scale parameter sequences. Using a result of Kakutani on equivalence of infinite product measures, we provide sufficient conditions for the equivalence of laws of the two sequences. arXiv 1608.03784 Numerical Mathematics
OPUS4-6023 misc Lie, Han Cheng; Sullivan, T. J. Cameron--Martin theorems for sequences of Cauchy-distributed random variables Given a sequence of Cauchy-distributed random variables defined by a sequence of location parameters and a sequence of scale parameters, we consider another sequence of random variables that is obtained by perturbing the location or scale parameter sequences. Using a result of Kakutani on equivalence of infinite product measures, we provide sufficient conditions for the equivalence of laws of the two sequences. urn:nbn:de:0297-zib-60230 Numerical Mathematics
OPUS4-5942 misc Sullivan, T. J. Well-posed Bayesian inverse problems and heavy-tailed stable Banach space priors This article extends the framework of Bayesian inverse problems in infinite-dimensional parameter spaces, as advocated by Stuart (Acta Numer. 19:451-559, 2010) and others, to the case of a heavy-tailed prior measure in the family of stable distributions, such as an infinite-dimensional Cauchy distribution, for which polynomial moments are infinite or undefined. It is shown that analogues of the Karhunen-Loève expansion for square-integrable random variables can be used to sample such measures. Furthermore, under weaker regularity assumptions than those used to date, the Bayesian posterior measure is shown to depend Lipschitz continuously in the Hellinger metric upon perturbations of the misfit function and observed data. 2016 urn:nbn:de:0297-zib-59422 10.3934/ipi.2017040 Numerical Mathematics
OPUS4-5951 misc Cockayne, Jon; Oates, Chris; Sullivan, T. J.; Girolami, Mark Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems This paper develops a class of meshless methods that are well-suited to statistical inverse problems involving partial differential equations (PDEs). The methods discussed in this paper view the forcing term in the PDE as a random field that induces a probability distribution over the residual error of a symmetric collocation method. This construction enables the solution of challenging inverse problems while accounting, in a rigorous way, for the impact of the discretisation of the forward problem. In particular, this confers robustness to failure of meshless methods, with statistical inferences driven to be more conservative in the presence of significant solver error. In addition, (i) a principled learning-theoretic approach to minimise the impact of solver error is developed, and (ii) the challenging setting of inverse problems with a non-linear forward model is considered. The method is applied to parameter inference problems in which non-negligible solver error must be accounted for in order to draw valid statistical conclusions. urn:nbn:de:0297-zib-59513 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
OPUS4-5807 Wissenschaftlicher Artikel Owhadi, Houman; Scovel, Clint; Sullivan, T. J. On the Brittleness of Bayesian Inference With the advent of high-performance computing, Bayesian methods are becoming increasingly popular tools for the quantification of uncertainty throughout science and industry. Since these methods can impact the making of sometimes critical decisions in increasingly complicated contexts, the sensitivity of their posterior conclusions with respect to the underlying models and prior beliefs is a pressing question to which there currently exist positive and negative answers. We report new results suggesting that, although Bayesian methods are robust when the number of possible outcomes is finite or when only a finite number of marginals of the data-generating distribution are unknown, they could be generically brittle when applied to continuous systems (and their discretizations) with finite information on the data-generating distribution. If closeness is defined in terms of the total variation (TV) metric or the matching of a finite system of generalized moments, then (1) two practitioners who use arbitrarily close models and observe the same (possibly arbitrarily large amount of) data may reach opposite conclusions; and (2) any given prior and model can be slightly perturbed to achieve any desired posterior conclusion. The mechanism causing brittleness/robustness suggests that learning and robustness are antagonistic requirements, which raises the possibility of a missing stability condition when using Bayesian inference in a continuous world under finite information. 16 SIAM Review 57 4 566 582 10.1137/130938633 Numerical Mathematics
OPUS4-5808 Buch (Monographie) Sullivan, T. J. Introduction to Uncertainty Quantification Springer 63 978-3-319-23394-9 10.1007/978-3-319-23395-6 Numerical Mathematics
OPUS4-6657 Wissenschaftlicher Artikel Lie, Han Cheng; Sullivan, T. J.; Stuart, Andrew Strong convergence rates of probabilistic integrators for ordinary differential equations Probabilistic integration of a continuous dynamical system is a way of systematically introducing model error, at scales no larger than errors inroduced by standard numerical discretisation, in order to enable thorough exploration of possible responses of the system to inputs. It is thus a potentially useful approach in a number of applications such as forward uncertainty quantification, inverse problems, and data assimilation. We extend the convergence analysis of probabilistic integrators for deterministic ordinary differential equations, as proposed by Conrad et al.\ (\textit{Stat.\ Comput.}, 2016), to establish mean-square convergence in the uniform norm on discrete- or continuous-time solutions under relaxed regularity assumptions on the driving vector fields and their induced flows. Specifically, we show that randomised high-order integrators for globally Lipschitz flows and randomised Euler integrators for dissipative vector fields with polynomially-bounded local Lipschitz constants all have the same mean-square convergence rate as their deterministic counterparts, provided that the variance of the integration noise is not of higher order than the corresponding deterministic integrator. Statistics and Computing Numerical Mathematics