Learning, Information Theory, and Nonequilibrium Statistical Mechanics

 
 

Berkeley workgroup on

       Learning, Information Theory, & Nonequilibrium

       Thermodynamics

 

Coordinates

EMAIL  lineq (at) lists.berkeley.edu

LOCATION  560 Evans Halls, UC Berkeley

TIme  3:30 PM every other Friday (kinda)

WEB https://calmail.berkeley.edu/manage/list/listinfo/lineq@lists.berkeley.edu

Nix Barnett (UCD): Structured Transformations of Structured Processes: The ε-Transducer

12 February 2016

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

Reference: http://arxiv.org/abs/1412.2690