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stat.ME2026
Computationally efficient segmentation for non-stationary time series with oscillatory patterns
Nicolas Bianco, Lorenzo Cappello
We propose a novel approach for change-point detection and parameter learning in multivariate non-stationary time series exhibiting oscillatory behaviour. We approximate the proces…
stat.ME2025
A Bayesian framework for change-point detection with uncertainty quantification
Davis Berlind, Lorenzo Cappello, Oscar Hernan Madrid Padilla
We introduce a novel Bayesian method that can detect multiple structural breaks in the mean and variance of a length time-series. Our method quantifies uncertainty by returning…
stat.ME2025
Bayesian variance change point detection with credible sets
Lorenzo Cappello, Oscar Hernan Madrid Padilla
This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point location…