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- Title
Computational Mechanics of Input-Output Processes: Structured Transformations and the $$\epsilon $$ -Transducer.
- Authors
Barnett, Nix; Crutchfield, James
- Abstract
Computational mechanics quantifies structure in a stochastic process via its causal states, leading to the process's minimal, optimal predictor-the $$\epsilon {\text {-}}\mathrm{machine}$$ . We extend computational mechanics to communication channels coupling two processes, obtaining an analogous optimal model-the $$\epsilon {\text {-}}\mathrm{transducer}$$ -of the stochastic mapping between them. Here, we lay the foundation of a structural analysis of communication channels, treating joint processes and processes with input. The result is a principled structural analysis of mechanisms that support information flow between processes. It is the first in a series on the structural information theory of memoryful channels, channel composition, and allied conditional information measures.
- Subjects
COMPUTATIONAL mechanics; MATHEMATICAL transformations; TRANSDUCERS; STOCHASTIC processes; MATHEMATICAL mappings
- Publication
Journal of Statistical Physics, 2015, Vol 161, Issue 2, p404
- ISSN
0022-4715
- Publication type
Article
- DOI
10.1007/s10955-015-1327-5