A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling
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Publication:6631124
DOI10.1080/00401706.2022.2127914MaRDI QIDQ6631124
Publication date: 31 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
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- Efficient scalable schemes for monitoring a large number of data streams
- Bayesian variable selection regression for genome-wide association studies and other large-scale problems
- High-dimensional additive modeling
- Bayesian estimation of sparse signals with a continuous spike-and-slab prior
- Composite Gaussian process models for emulating expensive functions
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- Dynamic variable selection with spike-and-slab process priors
- Spike and slab variable selection: frequentist and Bayesian strategies
- Functional data analysis.
- Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies
- Multivariate Statistical Process Control Using LASSO
- Statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation
- A Generalized Least-Square Matrix Decomposition
- EMVS: The EM Approach to Bayesian Variable Selection
- Frequentist Consistency of Variational Bayes
- A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams
- Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition
- Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
- Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
- Generalized additive models for large data sets
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