Pseudo-Observation Batch Selection
Cross-source consensus on Pseudo-Observation Batch Selection from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Evidence quality
Highlighted claims
- Constant Liar and Kriging Believer build batches by adding synthetic observations after each selected point. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Fantasy models use the same pseudo-observation principle but sample synthetic observations from the predictive distribution. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Kriging Believer leaves the posterior mean unchanged because its lie value equals the current predictive mean. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Constant Liar and Kriging Believer perform well empirically only when the surrogate is a Gaussian Process. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Constant Liar variants add mean shifts that help explain differences between CL-min, CL-max, and Kriging Believer. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not