Bayesian Online Change Point Detection

Bayesian Online Change Point Detection (BOCPD) focuses on identifying abrupt shifts in the underlying distribution of sequentially arriving data. Recent research emphasizes robust and scalable algorithms, often employing Bayesian methods with conjugate priors for efficient online updates, and extending BOCPD's applicability to more complex scenarios like multi-agent systems and non-stationary Markov Decision Processes. These advancements are improving the accuracy and speed of change point detection in diverse fields, including real-time spectrum sharing, reinforcement learning, and time series analysis where baseline shifts are common.