A Bayesian experimental design framework uses conditional density estimation and covariance filtering to compute expected information gain 6 to 13 times faster, demonstrated on surrogate modeling, parameter estimation, and failure probability benchmarks.
Global a-optimal robot exploration in slam
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
stat.ML 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Data
A Bayesian experimental design framework uses conditional density estimation and covariance filtering to compute expected information gain 6 to 13 times faster, demonstrated on surrogate modeling, parameter estimation, and failure probability benchmarks.