Criticality of mostly informative samples: A Bayesian model selection approach

TL;DR

This paper presents a Bayesian model selection approach to high-dimensional data, revealing that only certain partitions of observed states are distinguishable with limited samples, and that criticality relates to the relevance of state representations rather than self-organization.

Contribution

It introduces a Bayesian framework to distinguish between resolution and relevance in state representations, linking criticality to the relevance of samples without assuming self-organization.

Findings

Most relevant samples follow power law distributions.

Relevance of state partitions varies non-monotonically with resolution.

Criticality reflects state relevance, not self-organization.

Abstract

We discuss a Bayesian model selection approach to high dimensional data in the deep under sampling regime. The data is based on a representation of the possible discrete states $s$, as defined by the observer, and it consists of $M$ observations of the state. This approach shows that, for a given sample size $M$, not all states observed in the sample can be distinguished. Rather, only a partition of the sampled states $s$ can be resolved. Such partition defines an {\em emergent} classification $q_s$ of the states that becomes finer and finer as the sample size increases, through a process of {\em symmetry breaking} between states. This allows us to distinguish between the $resolution$ of a given representation of the observer defined states $s$, which is given by the entropy of $s$, and its $relevance$ which is defined by the entropy of the partition $q_s$. Relevance has a non-monotonic dependence on resolution, for a given sample size. In addition, we characterise most relevant samples and we show that they exhibit power law frequency distributions, generally taken as signatures of "criticality". This suggests that "criticality" reflects the relevance of a given representation of the states of a complex system, and does not necessarily require a specific mechanism of self-organisation to a critical point.