vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
Von Toussaint, Bayesian inference in physics, Reviews of Modern Physics 83 (2011) 943–999
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A bias-reduced Bayesian optimal experimental design procedure using Kullback-Leibler divergence is shown to select high-value steel mass-flow observations that reduce network-structure uncertainty in a U.S. steel MFA, with the optimum depending on total data budget.
Factorized sequential Bayesian updates on correlated pseudo-data produce systematic deviations from the joint posterior that increase with correlation strength, while exact conditional-likelihood updates match the joint result; an information decomposition attributes the mismatch to parameter-tuned,
citing papers explorer
-
Variational Sequential Optimal Experimental Design using Reinforcement Learning
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
-
Intelligent data collection for network discrimination in material flow analysis using Bayesian optimal experimental design
A bias-reduced Bayesian optimal experimental design procedure using Kullback-Leibler divergence is shown to select high-value steel mass-flow observations that reduce network-structure uncertainty in a U.S. steel MFA, with the optimum depending on total data budget.
-
Sequential Bayesian inference with correlated heavy-ion datasets
Factorized sequential Bayesian updates on correlated pseudo-data produce systematic deviations from the joint posterior that increase with correlation strength, while exact conditional-likelihood updates match the joint result; an information decomposition attributes the mismatch to parameter-tuned,