Optimizing sensor calibration in open environments: a Bayesian approach for non-specific multisensory systems
DOI10.5802/smai-jcm.114MaRDI QIDQ6658814
Bérengère Lebental, Marine Dumon, Guillaume Perrin
Publication date: 8 January 2025
Published in: The SMAI journal of computational mathematics (Search for Journal in Brave)
uncertainty quantificationBayesian frameworkair and water pollutiondata-driven calibrationmultisensory systems
Linear regression; mixed models (62J05) Bayesian inference (62F15) Determinants, permanents, traces, other special matrix functions (15A15) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Ecology (92D40) Dusty-gas two-phase flows (76T15) Reaction effects in flows (76V05) Processes in random environments (60K37) PDEs with randomness, stochastic partial differential equations (35R60) Applications of statistics to psychology (62P15)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Minimax and Minimax Projection Designs Using Clustering
- Feature significance for multivariate kernel density estimation
- The design and analysis of computer experiments.
- Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation
- Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints
- Approximate inference of the bandwidth in multivariate kernel density estimation
- Asymptotic Statistics
- Simulation and the Monte Carlo Method
- Introduction to nonparametric estimation
This page was built for publication: Optimizing sensor calibration in open environments: a Bayesian approach for non-specific multisensory systems