19 citations · 43 across the 8 of their papers we have counts for
9 papers
Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters
Daniel S. Han, Vaishnavi Ananthanarayanan, Matthew J. Simpson
Fractional Brownian motion (fBM) is a model of anomalous diffusion parameterised by a Hurst exponent and a generalised diffusion coefficient . We jointly est…
Parameter Estimation for Differential Equation Models Using Generalized Profiling: A Computational Tutorial
Matthew J Simpson, James S Bennett, Alexander Johnston +1
Parameter estimation connects mathematical models to real-world data and decision making across many scientific and industrial applications. Standard approaches such as maximum lik…
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…
Efficient inference for differential equation models without numerical solvers
Alexander Johnston, Ruth E. Baker, Matthew J. Simpson
Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtai…
Random walk models in the life sciences: including births, deaths and local interactions
Michael J. Plank, Matthew J. Simpson, Ruth E. Baker
Random walks and related spatial stochastic models have been used in a range of application areas including animal and plant ecology, infectious disease epidemiology, developmental…
Parameter identifiability, parameter estimation and model prediction for differential equation models
Matthew J Simpson, Ruth E Baker
Interpreting data with mathematical models is an important aspect of real-world industrial and applied mathematical modeling. Often we are interested to understand the extent to wh…
A generalised sigmoid population growth model with energy dependence: application to quantify the tipping point for Antarctic shallow seabed algae
Elise Mills, Graeme F. Clark, Matthew J. Simpson +2
Sigmoid growth models are often used to study population dynamics. The size of a population at equilibrium commonly depends explicitly on the availability of resources, such as an…
Coupling volume-excluding compartment-based models of diffusion at different scales: Voronoi and pseudo-compartment approaches
Paul R. Taylor, Ruth E. Baker, Matthew J. Simpson +1
Numerous processes across both the physical and biological sciences are driven by diffusion. Partial differential equations (PDEs) are a popular tool for modelling such phenomena d…
Reconciling transport models across scales: the role of volume exclusion
Paul R. Taylor, Christian A. Yates, Matthew J. Simpson +1
Diffusive transport is a universal phenomenon, throughout both biological and physical sciences, and models of diffusion are routinely used to interrogate diffusion-driven processe…