Markovletics: Methods and A Novel Application for Learning Continuous-Time Markov Chain Mixtures

Sequential data naturally arises from user engagement on digital platforms like social media, music streaming services, and web navigation, encapsulating evolving user preferences and behaviors through continuous information streams. A notable unresolved task in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs). While there is progress in learning mixtures of discrete-time Markov chains with recovery guarantees GKV16,ST23,KTT2023, the continuous scenario uncovers unique unexplored challenges. The intrigue in CTMC mixtures stems from their ability to model intricate continuous-time stochastic processes prevalent in various fields including social media, finance, and biology.