Order Markov

Order Markov models represent sequential data by considering dependencies between past and future events, aiming to accurately predict or understand temporal patterns. Current research focuses on developing more efficient and robust Markov models, including higher-order variants and novel architectures like predictive attractor models and subspace clustering methods, to address limitations in capacity and learning speed. These advancements have significant implications for diverse fields, improving the analysis of molecular dynamics, causal inference in time series data (e.g., neuroscience), and the design of more efficient artificial intelligence systems, particularly in natural language processing.