30 citations · 59 across the 7 of their papers we have counts for
7 papers
Robust Neural Posterior Estimation and Statistical Model Criticism
Daniel Ward, Patrick Cannon, Mark Beaumont +2
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes…
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Patrick Cannon, Daniel Ward, Sebastian M. Schmon
Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performin…
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
Jordan Langham-Lopez, Sebastian M. Schmon, Patrick Cannon
Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agen…
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inferenc…
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference…
Black-box Bayesian inference for economic agent-based models
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety…
Approximate Bayesian Computation with Path Signatures
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free post…