What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems. - Oliver J. Maclaren; Ruanui Nicholson (CoRR, 2019)

An Additive Approximation to Multiplicative Noise. - Ruanui Nicholson; Jari P. Kaipio (J. Math. Imaging Vis., 2020)

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty. - Alen Alexanderian; Ruanui Nicholson; Noemi Petra (CoRR, 2022)

Improved Electrical Impedance Tomography Reconstruction via a Bayesian Approach With an Anatomical Statistical Shape Model. - Mitchell I. Page; Ruanui Nicholson; Merryn H. Tawhai; Alys R. Clark; Haribalan Kumar (IEEE Trans. Biomed. Eng., 2023)

Non-intrusive optimal experimental design for large-scale nonlinear Bayesian inverse problems using a Bayesian approximation error approach. - Karina Koval; Ruanui Nicholson (CoRR, 2024)

Data space inversion for efficient predictions and uncertainty quantification for geothermal models. - Alex de Beer; Andrew Power; Daniel Wong; Ken Dekkers; Michael Gravatt; Elvar K. Bjarkason; John P. O'Sullivan; Michael J. O'Sullivan; Oliver J. Maclaren; Ruanui Nicholson (Comput. Geosci., 2025)