2016/08/16 by Bernhard C. Geiger, Yuchen Wu, Geiger, Bernhard C. +1
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning and Algorithms #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.1608.04637
openalex publication_date 2016/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of reducing a first-order Markov chain on a large alphabet to a higher-order Markov chain on a small alphabet. We present information-theoretic cost functions that are related to predictability and lumpability, show relations between these cost functions, and discuss heuristics to minimize them. Our experiments suggest that the generalization to higher orders is useful for model reduction in reliability analysis and natural language processing.