Markov Blankets

Mike's Notes

Pipi uses Markov chains, which can form a blanket. Blankets can be aggregated and nested. The Royal Society paper "The Markov Blankets of Life: autonomy, Active Inference and the Free Energy Principle" is excellent.

A Markov blanket statistically defines a system's boundaries (e.g., a cell or a multicellular organism). It is a statistical partitioning of a system into internal and external states, and the blanket itself consists of the states that separate the two.

Resources

• https://en.wikipedia.org/wiki/Markov_blanket
• https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0792
• https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0792?download=true (PDF)

Markov Blanket

"In statistics and machine learning, when one wants to infer a random variable with a set of variables, usually a subset is enough, and other variables are useless. Such a subset that contains all the useful information is called a Markov blanket. If a Markov blanket is minimal, meaning that it cannot drop any variable without losing information, it is called a Markov boundary. Identifying a Markov blanket or a Markov boundary helps to extract useful features. The terms of Markov blanket and Markov boundary were coined by Judea Pearl in 1988. A Markov blanket can be constituted by a set of Markov chains." - Wikipedia.

A Markov Chain Theory of Self Organization

By: Jacob Calvert, Georgia Tech University 

YouTube: 14 Nov 2024

Fundamentals of statistical mechanics explain that systems in thermal equilibrium spend more time in states with greater order because these states have lesser energy. This explanation is remarkable, and powerful, because energy is a "local" property of states. Nonequilibrium systems, like living systems, can also exhibit order, but there is no property analogous to energy that generally explains why states with greater order tend to emerge. However, recent experiments suggest that a local property called   rattling   predicts which states are favored, at least for a broad class of nonequilibrium systems. In this seminar, I will present a simple theory of rattling that explains when and why it works, and I will demonstrate its application to systems across scientific domains. Surprisingly, the core idea of rattling is so general as to apply to equilibrium and nonequilibrium systems alike. (Joint work with Dana Randall.)