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.
Quantifying uncertainty on pareto fronts with gaussian process conditional simulations.European journal of operational research, 243(2):386– 394, 2015
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.