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Patrick Cannon
Patrick Cannon
ML Research Scientist
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Titel
Zitiert von
Zitiert von
Jahr
Black-box Bayesian inference for economic agent-based models
J Dyer, P Cannon, JD Farmer, S Schmon
arXiv preprint arXiv:2202.00625, 2022
272022
Generalized Posteriors in Approximate Bayesian Computation
SM Schmon, PW Cannon, J Knoblauch
arXiv preprint arXiv:2011.08644, 2020
232020
Robust Neural Posterior Estimation and Statistical Model Criticism
D Ward, P Cannon, M Beaumont, M Fasiolo, SM Schmon
Neural Information Processing Systems 36, 2022
172022
Approximate Bayesian Computation with Path Signatures
J Dyer, P Cannon, SM Schmon
arXiv preprint arXiv:2106.12555, 2021
162021
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
P Cannon, D Ward, SM Schmon
arXiv preprint arXiv:2209.01845, 2022
152022
Calibrating Agent-based Models to Microdata with Graph Neural Networks
J Dyer, P Cannon, JD Farmer, SM Schmon
arXiv preprint arXiv:2206.07570, 2022
142022
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
J Dyer, P Cannon, SM Schmon
AISTATS, 2022, 2022
82022
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
82021
Black-box Bayesian inference for agent-based models
J Dyer, P Cannon, JD Farmer, SM Schmon
Journal of Economic Dynamics and Control, 104827, 2024
2024
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
J Langham-Lopez, SM Schmon, P Cannon
arXiv preprint arXiv:2207.03945, 2022
2022
A Particle Markov Chain Monte Carlo Approach to Coalescent Inference
PW Cannon
University of Bristol, 2019
2019
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