9 citations · 19 across the 16 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…
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…
Exact Sampling of Gibbs Measures with Estimated Losses
David T. Frazier, Jeremias Knoblauch, Jack Jewson +1
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of intere…