34 citations · 34 across the 5 of their papers we have counts for
5 papers
Rare event ABC-SMC
Ivis Kerama, Thomas Thorne, Richard G. Everitt
Approximate Bayesian computation (ABC) is a well-established family of Monte Carlo methods for performing approximate Bayesian inference in the case where an ``implicit'' model is…
Topological Approximate Bayesian Computation for Parameter Inference of an Angiogenesis Model
Thomas Thorne, Paul D. W. Kirk, Heather A. Harrington
Inferring the parameters of models describing biological systems is an important problem in the reverse engineering of the mechanisms underlying these systems. Much work has focuse…
Inference of Temporally Varying Bayesian Networks
Thomas Thorne, Michael P. H Stumpf
When analysing gene expression time series data an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Whilst some appro…
A parameter-free model discrimination criterion based on steady-state coplanarity
Heather A. Harrington, Kenneth L. Ho, Thomas Thorne +1
We describe a novel procedure for deciding when a mass-action model is incompatible with observed steady-state data that does not require any parameter estimation. Thus, we avoid t…
Considerate Approaches to Achieving Sufficiency for ABC model selection
Chris Barnes, Sarah Filippi, Michael P. H. Stumpf +1
For nearly any challenging scientific problem evaluation of the likelihood is problematic if not impossible. Approximate Bayesian computation (ABC) allows us to employ the whole Ba…