Computational Mechanics of Input-Output Processes: Structured transformations and the $\epsilon$-transducer

TL;DR

This paper extends computational mechanics to model communication channels between processes using the $$-transducer, providing a foundational structural analysis of information flow mechanisms in channels.

Contribution

It introduces the $$-transducer as a new optimal model for stochastic mappings between processes, expanding computational mechanics to channels with input and joint processes.

Findings

Defines the $$-transducer for channels

Establishes a structural analysis framework for information flow

Lays groundwork for future studies on memoryful channels

Abstract

Computational mechanics quantifies structure in a stochastic process via its causal states, leading to the process's minimal, optimal predictor---the $\epsilon$-machine. We extend computational mechanics to communication channels between two processes, obtaining an analogous optimal model---the $\epsilon$-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.