Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Cannon, Patrick"'
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes is often restricted by low data quality, as well as practical limits t
Externí odkaz:
http://arxiv.org/abs/2210.06564
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 performing flexible, black-box, approximate Bayesian inference for stochastic s
Externí odkaz:
http://arxiv.org/abs/2209.01845
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. In this paper we demonstrate the use of Vogue, a high performance
Externí odkaz:
http://arxiv.org/abs/2207.03945
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 inference methods have emerged as powerful tools for performing this task when
Externí odkaz:
http://arxiv.org/abs/2206.07570
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel
Externí odkaz:
http://arxiv.org/abs/2202.11585
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 of empirically observed behaviours of complex systems, give them broad
Externí odkaz:
http://arxiv.org/abs/2202.00625
Publikováno v:
In Journal of Economic Dynamics and Control April 2024 161
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free posterior samples by comparing simulated and observed data through some di
Externí odkaz:
http://arxiv.org/abs/2106.12555
Autor:
Chu, Zachary, Cosset, Cindy C.P., Finlayson, Catherine, Cannon, Patrick G., Freckleton, Robert P., Yusah, Kalsum M., Edwards, David P.
Publikováno v:
In Biological Conservation March 2024 291
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunately, they typically lack the tractability required for conventional sta
Externí odkaz:
http://arxiv.org/abs/2011.08644