2011/11/18 by Nicholas F. Travers, James P. Crutchfield, Travers, Nicholas F. +1 · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Cellular Automata and Applications #Chaotic Dynamics (nlin.CD) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #Probability (math.PR) #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #cs.IT #math.IT #math.PR #nlin.CD #stat.ML
paper · pdf · doi:10.48550/arxiv.1111.4500
23 pages, 5 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/hgem.htm; Expanded literature review, additional examples and figures
openalex publication_date 2011/11/18 · arxiv created 2012/12/14 · arxiv updated 2015/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though, an alternative generator definition was given: unifilar, edge-emitting hidden Markov models with probabilistically distinct states. The key difference is that history epsilon-machines are defined by a process, whereas generator epsilon-machines define a process. We show here that these two definitions are equivalent in the finite-state case.