Markovian Sequence

Markovian sequences, characterized by the property that future states depend only on the present state, are a fundamental concept with applications across diverse fields. Current research focuses on improving the efficiency and robustness of algorithms dealing with Markovian processes, particularly in areas like estimating missing data, developing robust decision-making strategies under uncertainty (e.g., in Markov Decision Processes), and addressing challenges arising from non-Markovian systems through state augmentation or alternative modeling techniques. These advancements are crucial for improving the accuracy and reliability of machine learning models, particularly in applications involving sequential data and dynamic systems, such as natural language processing and control systems.