Markov Model

Markov models are probabilistic models that describe systems transitioning between states, with the probability of transitioning to a future state depending only on the current state (the Markov property). Current research focuses on extending Markov models to handle diverse data types and complexities, including time-inhomogeneity, high dimensionality, and non-Markov reward functions, often employing techniques like hidden Markov models, mixture models, and deep learning for improved accuracy and efficiency. These advancements are impacting various fields, from improving the robustness of autonomous systems and enhancing large language model reliability to enabling more accurate modeling of disease progression and resource availability in smart cities. The development of novel algorithms and model architectures continues to broaden the applicability and improve the performance of Markov models across numerous scientific and engineering domains.