Identifying indicators of chemical, biological, radiological, and nuclear (CBRN) weapons development activity in sub-national terrorist groups. - Timothy John Sullivan; W. L. Perry (J. Oper. Res. Soc., 2004)

A knowledge-based approach for classifying lake water chemistry. - Michael C. Saunders; Timothy John Sullivan; B. L. Nash; K. A. Tonnessen; B. J. Miller (Knowl. Based Syst., 2005)

Application of a regionalized knowledge-based model for classifying the impacts of nitrogen, sulfur, and organic acids on lakewater chemistry. - Timothy John Sullivan; Michael C. Saunders; K. A. Tonnessen; B. L. Nash; B. J. Miller (Knowl. Based Syst., 2005)

Optimal uncertainty quantification for legacy data observations of Lipschitz functions. - Timothy John Sullivan; Mike McKerns; Dominik Meyer; Florian Theil; Houman Owhadi; Michael Ortiz (CoRR, 2012)

The Optimal Uncertainty Algorithm in the Mystic Framework - Mike McKerns; Houman Owhadi; Clint Scovel; Timothy John Sullivan; Michael Ortiz (CoRR, 2012)

Optimal Uncertainty Quantification. - Houman Owhadi; Clint Scovel; Timothy John Sullivan; Mike McKerns; Michael Ortiz (SIAM Rev., 2013)

On the Brittleness of Bayesian Inference. - Houman Owhadi; Clint Scovel; Timothy John Sullivan (SIAM Rev., 2015)

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2016)

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2017)

Implicit Probabilistic Integrators for ODEs. - Onur Teymur; Han Cheng Lie; Tim Sullivan 001; Ben Calderhead (NeurIPS, 2018)

Random Forward Models and Log-Likelihoods in Bayesian Inverse Problems. - Han Cheng Lie; Timothy John Sullivan; Aretha L. Teckentrup (SIAM/ASA J. Uncertain. Quantification, 2018)

Error Bounds for Some Approximate Posterior Measures in Bayesian Inference. - Han Cheng Lie; Timothy John Sullivan; Aretha L. Teckentrup (ENUMATH, 2019)

Comments on the article "A Bayesian conjugate gradient method". - Timothy John Sullivan (CoRR, 2019)

Editorial: special edition on probabilistic numerics. - Mark A. Girolami; Ilse C. F. Ipsen; Chris J. Oates; Art B. Owen; Timothy John Sullivan (Stat. Comput., 2019)

Strong convergence rates of probabilistic integrators for ordinary differential equations. - Han Cheng Lie; Andrew M. Stuart; Timothy John Sullivan (Stat. Comput., 2019)

A modern retrospective on probabilistic numerics. - Chris J. Oates; Timothy John Sullivan (Stat. Comput., 2019)

Bayesian Probabilistic Numerical Methods. - Jon Cockayne; Chris J. Oates; Timothy John Sullivan; Mark A. Girolami (SIAM Rev., 2019)

Adaptive Reconstruction of Imperfectly Observed Monotone Functions, with Applications to Uncertainty Quantification. - Luc Bonnet; Jean-Luc Akian; Éric Savin; Timothy John Sullivan (Algorithms, 2020)

Optimal Bounds on Nonlinear Partial Differential Equations in Model Certification, Validation, and Experimental Design. - Mike McKerns; Francis J. Alexander; Kyle S. Hickmann; Timothy John Sullivan; Diane E. Vaughan (CoRR, 2020)

Geodesic Analysis in Kendall's Shape Space with Epidemiological Applications. - Esfandiar Nava-Yazdani; Hans-Christian Hege; Timothy John Sullivan; Christoph von Tycowicz (J. Math. Imaging Vis., 2020)

Convergence rates of Gaussian ODE filters. - Hans Kersting; Timothy John Sullivan; Philipp Hennig (Stat. Comput., 2020)

A Rigorous Theory of Conditional Mean Embeddings. - Ilja Klebanov; Ingmar Schuster; Timothy John Sullivan (SIAM J. Math. Data Sci., 2020)

Randomised one-step time integration methods for deterministic operator differential equations. - Han Cheng Lie; Martin Stahn; Timothy John Sullivan (CoRR, 2021)

Γ-convergence of Onsager-Machlup functionals. Part I: With applications to maximum a posteriori estimation in Bayesian inverse problems. - Birzhan Ayanbayev; Ilja Klebanov; Han Cheng Lie; Timothy John Sullivan (CoRR, 2021)

Γ-convergence of Onsager-Machlup functionals. Part II: Infinite product measures on Banach spaces. - Birzhan Ayanbayev; Ilja Klebanov; Han Cheng Lie; Timothy John Sullivan (CoRR, 2021)

Dimension-independent Markov chain Monte Carlo on the sphere. - Han Cheng Lie; Daniel Rudolf; Björn Sprungk; Timothy John Sullivan (CoRR, 2021)

Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432). - Philipp Hennig; Ilse C. F. Ipsen; Maren Mahsereci; Tim Sullivan 001 (Dagstuhl Reports, 2021)

Compression, Inversion, and Approximate PCA of Dense Kernel Matrices at Near-Linear Computational Complexity. - Florian Schäfer 001; Timothy John Sullivan; Houman Owhadi (Multiscale Model. Simul., 2021)

Bayesian numerical methods for nonlinear partial differential equations. - Junyang Wang; Jon Cockayne; Oksana A. Chkrebtii; Timothy John Sullivan; Chris J. Oates (Stat. Comput., 2021)

Testing Whether a Learning Procedure is Calibrated. - Jon Cockayne; Matthew M. Graham; Chris J. Oates; Timothy John Sullivan; Onur Teymur (J. Mach. Learn. Res., 2022)

Transporting Higher-Order Quadrature Rules: Quasi-Monte Carlo Points and Sparse Grids for Mixture Distributions. - Ilja Klebanov; Timothy John Sullivan (CoRR, 2023)

GParareal: a time-parallel ODE solver using Gaussian process emulation. - Kamran Pentland; Massimiliano Tamborrino; Timothy John Sullivan; James Buchanan; Lynton C. Appel (Stat. Comput., 2023)

Error Bound Analysis of the Stochastic Parareal Algorithm. - Kamran Pentland; Massimiliano Tamborrino; Timothy John Sullivan (SIAM J. Sci. Comput., 2023)

An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems. - Hefin Lambley; Timothy John Sullivan (SIAM/ASA J. Uncertain. Quantification, 2023)

Autoencoders in Function Space. - Justin Bunker; Mark Girolami; Hefin Lambley; Andrew M. Stuart; Tim Sullivan 001 (CoRR, 2024)