Proposes a trajectory-oriented Bayesian optimization method using Gaussian process surrogates on parameters and seeds with adaptive Thompson sampling for efficient discovery of data-consistent trajectories in stochastic epidemic models.
Group kernels for gaussian process metamodels with categorical inputs.SIAM/ASA Journal on Uncertainty Quantification, 8(2):775–806, 2020
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Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Proposes a trajectory-oriented Bayesian optimization method using Gaussian process surrogates on parameters and seeds with adaptive Thompson sampling for efficient discovery of data-consistent trajectories in stochastic epidemic models.