A Bayesian optimization method with heteroscedastic Gaussian process surrogate and movement switching cost achieves sublinear regret and effective source localization in simulations for radioactive source seeking.
Gaussian Processes for Machine Learning , year =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.app-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
A Bayesian optimization method with heteroscedastic Gaussian process surrogate and movement switching cost achieves sublinear regret and effective source localization in simulations for radioactive source seeking.