UC Davis

Computational mechanics quantifies structure in a stochastic process via its causal states, leading to the process’s minimal, optimal predictor—the ϵ-machine$$\epsilon {\text {-}}\mathrm{machine}$$. We extend computational mechanics to communication channels coupling two processes, obtaining an analogous optimal model—the ϵ-transducer$$\epsilon {\text {-}}\mathrm{transducer}$$—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.