Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Bayesian online changepoint detection
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.
years
2026 9representative citing papers
Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.
CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.
A three-regime causal model with a latent build-up phase enables a MAX-aggregation trigger detector to deliver positive expected lead time before observable stress in limit order books.
BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
Pi-Change extends the PELT framework for multiple change point detection by incorporating prior information on locations through a time-varying penalty that preserves dynamic programming efficiency.
Posterior learning debt enables cost-sensitive retraining decisions that outperform calendar-based and CUSUM methods in synthetic Bayesian simulations.
A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.
citing papers explorer
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Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.
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CHASM: Online Changepoint Detection in Temporal and Cross-Variable Dependence
CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.
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Early Detection of Latent Microstructure Regimes in Limit Order Books
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Online Bayesian Calibration under Gradual and Abrupt System Changes
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Sequential Bayesian Monitoring for Recoverable and Drifting Processes
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
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Pi-Change: A Prior-Informed Multiple Change Point Detection Algorithm
Pi-Change extends the PELT framework for multiple change point detection by incorporating prior information on locations through a time-varying penalty that preserves dynamic programming efficiency.
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Cost-sensitive retraining via posterior learning debt
Posterior learning debt enables cost-sensitive retraining decisions that outperform calendar-based and CUSUM methods in synthetic Bayesian simulations.
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Data-driven moving-window Bayesian inference for transient CO2-temperature network models of buildings
A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.