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Bayesian calibration of stochastic agent based model via random forest
Agent-based models (ABM) provide an excellent framework for modeling outbreaks and
interventions in epidemiology by explicitly accounting for diverse individual interactions and …
interventions in epidemiology by explicitly accounting for diverse individual interactions and …
Uncertainty Quantification for Agent Based Models: A Tutorial
We explore the application of uncertainty quantification methods to agent-based models
(ABMs) using a simple sheep and wolf predator-prey model. This work serves as a tutorial …
(ABMs) using a simple sheep and wolf predator-prey model. This work serves as a tutorial …
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational
methodologies utilized for state estimation and parameter inference within dynamical …
methodologies utilized for state estimation and parameter inference within dynamical …
Gearing Gaussian process modeling and sequential design towards stochastic simulators
This chapter presents specific aspects of Gaussian process modeling in the presence of
complex noise. Starting from the standard homoscedastic model, various generalizations …
complex noise. Starting from the standard homoscedastic model, various generalizations …
Distributed Model Exploration with EMEWS
As high-performance computing resources have become increasingly available, new modes
of applying and experimenting with simulation and other computational tools have become …
of applying and experimenting with simulation and other computational tools have become …