Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound

DOI10.1137/15M1043303zbMath1370.62019arXiv1506.00053MaRDI QIDQ5269853

Roger G. Ghanem, Paris Hajali, Panagiotis Tsilifis

Publication date: 28 June 2017

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.00053

zbMATH Keywords

stochastic optimizationMonte Carlo samplingpolynomial chaosBayesian experimental designtwo-phase transportexpected information gain

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) Measures of information, entropy (94A17)

Related Items (13)

Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework ⋮ Reduced Wiener chaos representation of random fields via basis adaptation and projection ⋮ Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning ⋮ The stochastic quasi-chemical model for bacterial growth: variational Bayesian parameter update ⋮ Compressive sensing adaptation for polynomial chaos expansions ⋮ Bayesian adaptation of chaos representations using variational inference and sampling on geodesics ⋮ Multilevel Monte Carlo estimation of expected information gains ⋮ An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties ⋮ Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian processes ⋮ Optimal Bayesian experimental design for subsurface flow problems ⋮ Optimal Bayesian experimental design for electrical impedance tomography in medical imaging ⋮ Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems ⋮ Parallel Simultaneous Perturbation Optimization

Uses Software

• TOUGH
• Groundwater Modeling System

Cites Work

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