Installation
About
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)
Citation | bayesWatch citation info |
Copyright | file COPYRIGHTS |
Key Metrics
Downloads
Yesterday | 7 +75% |
Last 7 days | 70 -4% |
Last 30 days | 243 -24% |
Last 90 days | 904 -27% |
Last 365 days | 2.217 |