Probabilistic Soil Strata Delineation Using DPT Data and Bayesian Changepoint Detection

Bayesian Online Changepoint Detection

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. Her...

Bayesian approach to multilayer stochastic blockmodel and network changepoint detection

Soil total phosphorus threshold in the Everglades: a Bayesian changepoint analysis for multinomial response data

The Everglades is a historically phosphorus limited freshwater wetland ecosystem. Increasing the supply of phosphorus will lead to changes in the ecosystem. As a cumulative indicator of phosphorus loading to the wetland system, the top 10 cm soil TP concentration is often used to indicate eutrophication. The current study used multivariate species composition data to find the TP threshold, abov...

CHAMP: Changepoint Detection Using Approximate Model Parameters

We introduce CHAMP, an algorithm for online Bayesian changepoint detection in settings where it is difficult or undesirable to integrate over the parameters of candidate models. Rather than requiring integration of the parameters of candidate models as in several other Bayesian approaches, we require only the ability to fit model parameters to data segments. This approach greatly simplifies the...

State-of-the-Art in Bayesian Changepoint Detection

We provide a brief overview of the state-of-the-art in quickest (sequential) changepoint detection and present some new results on asymptotic and numerical analysis of main competitors such as the CUSUM, Shiryaev–Roberts, and Shiryaev detection procedures in a Bayesian context.