19 citations · 56 across the 9 of their papers we have counts for
9 papers
Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
Ruanui Nicholson, Matti Niskanen, Oliver J. Maclaren +1
We consider joint inversion for two or more unknown parameters from observational data in the Bayesian framework. Standard approaches often either treat the parameters as independe…
Invariant Image Reparameterisation: Bridging Symbolic and Numerical Methods for Identifiability Analysis, Model Reduction, and Prediction
Oliver J. Maclaren, Ruanui Nicholson, Joel A. Trent +2
Structural and practical parameter non-identifiability issues are common when mathematical models are used to interpret data. Such issues motivate model reparameterisation and redu…
Ensemble Kalman Inversion for Geothermal Reservoir Modelling
Alex de Beer, Elvar K Bjarkason, Michael Gravatt +4
Numerical models of geothermal reservoirs typically depend on hundreds or thousands of unknown parameters, which must be estimated using sparse, noisy data. However, these models c…
Data Space Inversion for Efficient Predictions and Uncertainty Quantification for Geothermal Models
Alex de Beer, Andrew Power, Daniel Wong +7
The ability to make accurate predictions with quantified uncertainty provides a crucial foundation for the successful management of a geothermal reservoir. Conventional approaches…
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Oliver J. Maclaren, Ruanui Nicholson
We consider basic conceptual questions concerning the relationship between statistical estimation and causal inference. Firstly, we show how to translate causal inference problems…
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 +2
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 (MC…
Is profile likelihood a true likelihood? An argument in favor
Oliver J. Maclaren
Profile likelihood is the key tool for dealing with nuisance parameters in likelihood theory. It is often asserted, however, that profile likelihood is not a 'true' likelihood. One…
Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching
Elvar K. Bjarkason, Oliver J. Maclaren, John P. O'Sullivan +1
The Levenberg-Marquardt (LM) method is commonly used for inverting models used to describe geothermal, groundwater, or oil and gas reservoirs. In previous studies LM parameter upda…
Models, measurement and inference in epithelial tissue dynamics
O. J. Maclaren, A. G. Fletcher, H. M. Byrne +1
The majority of solid tumours arise in epithelia and therefore much research effort has gone into investigating the growth, renewal and regulation of these tissues. Here we review…