Information Flow in Markov Chains

We consider the problem of characterizing the flow of information in stochastic systems. Recently, several measures of partial information decomposition (PID) have been proposed which, for a fixed target variable, can distinguish unique, redundant, and synergistic contributions from the predictor va...

Full description

Saved in:
Bibliographic Details
Main Authors: Niu, Xueyan, Quinn, Christopher J.
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Markov processes
Mutual information
Rivers
Stochastic systems
Topology
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider the problem of characterizing the flow of information in stochastic systems. Recently, several measures of partial information decomposition (PID) have been proposed which, for a fixed target variable, can distinguish unique, redundant, and synergistic contributions from the predictor variables. We study how each of those partial informations travel in a Markov chain, entering at one variable, passing through several variables, and eventually exiting downstream. Our work is agnostic to specific partial information decomposition (PID) measures. We investigate partial information flow among variables relating to overflow events in a river system.
ISSN:2576-2370
DOI:10.1109/CDC45484.2021.9683569