Continuous Time Markov Chain

Continuous-time Markov chains (CTMCs) model systems transitioning between states at irregular time intervals, finding applications across diverse fields from molecular dynamics to user behavior analysis. Current research emphasizes developing efficient algorithms for learning CTMC parameters from data, particularly focusing on mixtures of CTMCs and adapting CTMC frameworks to discrete diffusion models for generative modeling tasks. These advancements improve the accuracy and scalability of CTMC-based inference and modeling, impacting areas like biological modeling, generative AI, and the analysis of complex systems.