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- Publications
- Influence

New Graphs with Thinly Spread Positive Combinatorial Curvature

- J. Sneddon, Ruanui Nicholson
- Mathematics
- 2011

The combinatorial curvature at a vertex v of a plane graph G is defined as KG(v) = 1−v/2+ ∑ f∼v 1/|f |. As a consequence of Euler’s formula, the total curvature of a plane graph is 2. In 2008, Zhang… Expand

- 13
- 2
- Open Access

Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field

- Ruanui Nicholson, N. Petra, J. Kaipio
- Mathematics
- 11 January 2018

We consider the reconstruction of a heterogeneous coefficient field in a Robin boundary condition on an inaccessible part of the boundary in a Poisson problem with an uncertain (or unknown)… Expand

What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems

- Oliver J. Maclaren, Ruanui Nicholson
- Mathematics, Computer Science
- ArXiv
- 4 April 2019

Here we consider, in the context of causal inference, the basic question: 'what can be estimated from data?'. We call this the question of estimability. We consider the usual definition adopted in… Expand

An Additive Approximation to Multiplicative Noise

- Ruanui Nicholson, J. Kaipio
- Computer Science, Mathematics
- Journal of Mathematical Imaging and Vision
- 7 May 2018

Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with relatively small… Expand

Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification

- Oliver J. Maclaren, Ruanui Nicholson, Elvar K. Bjarkason, J. O'Sullivan, M. O'Sullivan
- Mathematics, Computer Science
- 10 October 2018

We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our goal is to make standard, 'out-of-the-box' Markov chain Monte Carlo (MCMC) sampling more… Expand

A calibration transform for families of spectroscopes

- S. Taylor, Boris Bäumer, Ruanui Nicholson
- Mathematics
- 31 July 2017

We seek a transform that will map between the outputs of pairs of similar spectroscopes. The spectroscopes are used to sort fruit and must regularly be calibrated to allow for seasonal fruit… Expand

Accounting for Modelling Errors in Parameter Estimation Problems: The Bayesian Approximation Error Approach

- Ruanui Nicholson, Anton Gulley, J. Kaipio, J. Eccles
- Computer Science
- 21 November 2016

Many parameter estimation problems are highly sensitive to errors. The Bayesian framework provides a methodology for incorporating these errors into our inversion. However, how to characterise the… Expand

Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty

We consider the problem of inferring the basal sliding coefficient field for an uncertain Stokes ice sheet forward model from surface velocity measurements. The uncertainty in the forward model stems… Expand

Incorporating Posterior-Informed Approximation Errors into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification.

- Oliver J. Maclaren, Ruanui Nicholson, Elvar K. Bjarkason, J. O'Sullivan, M. O'Sullivan
- Geology, Mathematics
- 22 December 2019

We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, `out-of-the-box' Markov chain Monte Carlo (MCMC) sampling more… Expand