Provides tools for change-point detection in the sequential setting and simulation experiments. The observations are assumed to be independent and such that each follows a known common distribution before the change, and a different common distribution after the change. We have proposed a statistic-based stopping rule for continuous and binary data with known post-changed distributions. For continuous data with unknown post-changed distributions, we have developed a generalized Bayesian stopping rule implemented via the Sequential Monte Carlo algorithm. The proposed methods utilize the Hyvarinen score to improve computation efficiency when the model is known up to a factor of normalizing constant.
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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