102 citations · 277 across the 26 of their papers we have counts for
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The Statistical Accuracy of Neural Posterior and Likelihood Estimation
David T. Frazier, Ryan Kelly, Christopher Drovandi +1
Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex…
A Comprehensive Guide to Simulation-based Inference in Computational Biology
Xiaoyu Wang, Ryan P. Kelly, Adrianne L. Jenner +2
Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially w…
Bayesian design for mathematical models of fruit growth based on misspecified prior information
Nushrath Najimuddin, David J. Warne, Helen Thompson +1
Bayesian design can be used for efficient data collection over time when the process can be described by the solution to an ordinary differential equation (ODE). Typically, Bayesia…
Mathematical modelling and uncertainty quantification for analysis of biphasic coral reef recovery patterns
David J. Warne, Kerryn Crossman, Grace E. M. Heron +6
Coral reefs are increasingly subjected to major disturbances threatening the health of marine ecosystems. Substantial research underway to develop intervention strategies that assi…
Likelihood-based inference, identifiability and prediction using count data from lattice-based random walk models
Yihan Liu, David J Warne, Matthew J Simpson
In vitro cell biology experiments are routinely used to characterize cell migration properties under various experimental conditions. These experiments can be interpreted using lat…
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
Xiaoyu Wang, Ryan P. Kelly, David J. Warne +1
Simulation based inference (SBI) methods enable the estimation of posterior distributions when the likelihood function is intractable, but where model simulation is feasible. Popul…