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]
Noted in “The free-energy principle: Can one idea explain why everything exists?” Elise Cutts, NewScientist (19 October 2024, paywall):
To divide the brain from the world it models, Friston implemented another mathematical tool: the Markov blanket. This acts as a sort of causal go-between, determining the relevant information that defines a particular brain state …. Depending on the scale you are interested in, a brain state could be something as granular as whether a particular neuron is firing or as enormous as depression.
Hmmmmmmm. I seem to remember something about Markov chains, which is part of this concept, when I was messing about on Kaggle. Don’t bother to ask, I wasn’t any good at it.
