Research map / Probabilistic and scientific modeling / Bayesian optimization
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use myopic posterior sampling (sample theta from posterior, act greedily) instead of posterior-mean expectation | MPS for bandit problems · Bandits (general discussion) | regret | Helped |
| Reduce MPS to Thompson sampling in bandit and Bayesian optimisation special cases | MPS · Bandits & Bayesian optimisation | no reported metric | No clear effect |
| Assume Condition 1 (asymptotically similar reward across parameter values, tau_n in o(n)) so MPS achieves sublinear regret against myopic optimal policy | MPS · Theory | cumulative regret bound | Helped |
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