Isotonic surrogate modeling for computer experiments with many input variables
stat.ME, math.ST, stat.CO, stat.ML, stat.TH
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- BdryGP: a new Gaussian process model for incorporating boundary information
- Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
- Adaptive Resolution for Finite-Rank Gaussian Processes
- Constrained Gaussian Random Fields with Continuous Linear Boundary Restrictions for Physics-informed Modeling of States
- Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators
- MCMC Methods for Parameter Inference in Structurally Nonidentifiable Models
Related papers
- Doubly robust inference via calibration
- Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
- Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
- Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance
- A Survey on Archetypal Analysis
- Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States