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Found 7 papers, 1 papers with code
Robust Neural Posterior Estimation and Statistical Model Criticism
In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models, and consider the implication of a simulation-to-reality gap.
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
In this work, we provide the first comprehensive study of the behaviour of neural SBI algorithms in the presence of various forms of model misspecification.
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose.
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible.
Black-box Bayesian inference for economic agent-based models
We present benchmarking experiments in which we demonstrate that neural network based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate time-series data.
Approximate Bayesian Computation with Path Signatures
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable.
