bayesWatch - Bayesian Change-Point Detection for Process Monitoring with
Fault Detection
Bayes Watch fits an array of Gaussian Graphical Mixture
Models to groupings of homogeneous data in time, called
regimes, which are modeled as the observed states of a Markov
process with unknown transition probabilities. In doing so,
Bayes Watch defines a posterior distribution on a vector of
regime assignments, which gives meaningful expressions on the
probability of every possible change-point. Bayes Watch also
allows for an effective and efficient fault detection system
that assesses what features in the data where the most
responsible for a given change-point. For further details,
see: Alexander C. Murph et al. (2023)
<doi:10.48550/arXiv.2310.02940>.
