Black-box Bayesian inference for agent-based models J Dyer, P Cannon, JD Farmer, SM Schmon Journal of Economic Dynamics and Control 161, 104827, 2024 | 31 | 2024 |
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference P Cannon, D Ward, SM Schmon arXiv preprint arXiv:2209.01845, 2022 | 24 | 2022 |
Generalized Posteriors in Approximate Bayesian Computation SM Schmon, PW Cannon, J Knoblauch Third Symposium on Advances in Approximate Bayesian Inference (AABI), 2021 | 24 | 2021 |
Robust Neural Posterior Estimation and Statistical Model Criticism D Ward, P Cannon, M Beaumont, M Fasiolo, SM Schmon Neural Information Processing Systems 36, 2022 | 21 | 2022 |
Approximate Bayesian Computation with Path Signatures J Dyer, P Cannon, SM Schmon arXiv preprint arXiv:2106.12555, 2021 | 16 | 2021 |
Calibrating Agent-based Models to Microdata with Graph Neural Networks J Dyer, P Cannon, JD Farmer, SM Schmon ICML, AI for Agent-based Models Workshop (AI4ABM), 2022 | 15 | 2022 |
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation J Dyer, P Cannon, SM Schmon AISTATS, 2022, 2022 | 8 | 2022 |
Deep Signature Statistics for Likelihood-free Time-series Models J Dyer, PW Cannon, SM Schmon ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | 8 | 2021 |
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning J Langham-Lopez, SM Schmon, P Cannon ICML, AI for Agent-based Models Workshop (AI4ABM), 2022 | | 2022 |
A Particle Markov Chain Monte Carlo Approach to Coalescent Inference PW Cannon University of Bristol, 2019 | | 2019 |