Visualization for Dichotomous Variables, the Independence and Markov chains

TL;DR

This paper explores visualization techniques for understanding independence and Markov chains among dichotomous variables, emphasizing a novel approach to plotting all events of the process in a single diagram.

Contribution

It introduces a new visualization method for Markov chains with dichotomous variables, focusing on representing all events in one comprehensive plot.

Findings

Visualization clarifies independence relationships

Method generalizes to other discrete variables

Enhances understanding of Markov process structure

Abstract

In probability theory, the independence is a very fundamental concept, but with a little mystery. People can always easily manipulate it logistically but not geometrically, especially when it comes to the independence relationships among more that two variables, which may also involve conditional independence. Here I am particularly interested in visualizing Markov chains which have the well known memoryless property. I am not talking about drawing the transition graph, instead, I will draw all events of the Markov process in a single plot. Here, to simplify the question, this work will only consider dichotomous variables, but all the methods actually can be generalized to arbitrary set of discrete variables.