J
James P. Crutchfield
Researcher at University of California, Davis
Publications - 338
Citations - 20738
James P. Crutchfield is an academic researcher from University of California, Davis. The author has contributed to research in topics: Entropy rate & Dynamical systems theory. The author has an hindex of 62, co-authored 314 publications receiving 19299 citations. Previous affiliations of James P. Crutchfield include University of California, Santa Cruz & PARC.
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Enumerating Finitary Processes
TL;DR: It is shown how to efficiently enumerate a class of finite-memory stochastic processes using the causal representation of epsilon-machines, characterized in the language of automata theory and adapted to a recent algorithm for generating accessible deterministic finite automata.
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Hierarchical Self-Organization in the Finitary Process Soup
TL;DR: In this paper, the authors investigated the hypothesis that the diversity of organismal character is due to hierarchical organization and showed that the process of structural innovation is facilitated by the discovery and maintenance of relatively noncomplex, but general individuals in a population.
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Informational and Causal Architecture of Continuous-time Renewal and Hidden Semi-Markov Processes
TL;DR: In this article, the authors introduce the minimal maximally predictive models (machines) of continuous-time renewal processes generated by hidden semi-Markov models, and present a complete analysis of the machines.
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Hierarchical self-organization in the finitary process soup
TL;DR: It is shown that global complexity in the finitary process soup is due to the emergence of successively higher levels of organization, that the hierarchical structure appears spontaneously, and that the process of structural innovation is facilitated by the discovery and maintenance of relatively noncomplex, but general, individuals in a population.
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Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes
TL;DR: Among the predictive hidden Markov models that describe a given stochastic process, the Renyi-based memory measure is strongly minimal in that it minimizes every Renyi based memory measure as discussed by the authors.