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New Graphs with Thinly Spread Positive Combinatorial Curvature
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, ZhangExpand
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  • Open Access
Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field
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
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  • Open Access
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
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 inExpand
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  • Open Access
An Additive Approximation to Multiplicative Noise
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 smallExpand
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  • Open Access
Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification
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 moreExpand
  • 1
  • Open Access
A calibration transform for families of spectroscopes
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 fruitExpand
Accounting for Modelling Errors in Parameter Estimation Problems: The Bayesian Approximation Error Approach
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 theExpand
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 stemsExpand
Incorporating Posterior-Informed Approximation Errors into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification.
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 moreExpand
Approaches to Multiscale Inverse Problems
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