Parametric inference in the large data limit using maximally informative models

TL;DR

This paper introduces a mutual information-based approach for inferring filters in high-dimensional data, overcoming the need for precise noise models and revealing fundamental substructures in parameter space.

Contribution

It demonstrates that mutual information maximization can replace likelihood-based inference in large data limits, identifying diffeomorphic modes that standard methods overlook.

Findings

Mutual information maximization avoids bias from incorrect noise assumptions.

Identifies and characterizes diffeomorphic modes in parameter space.

Provides a systematic way to derive these modes.

Abstract

Motivated by data-rich experiments in transcriptional regulation and sensory neuroscience, we consider the following general problem in statistical inference. When exposed to a high-dimensional signal S, a system of interest computes a representation R of that signal which is then observed through a noisy measurement M. From a large number of signals and measurements, we wish to infer the "filter" that maps S to R. However, the standard method for solving such problems, likelihood-based inference, requires perfect a priori knowledge of the "noise function" mapping R to M. In practice such noise functions are usually known only approximately, if at all, and using an incorrect noise function will typically bias the inferred filter. Here we show that, in the large data limit, this need for a pre-characterized noise function can be circumvented by searching for filters that instead maximize the mutual information I[M;R] between observed measurements and predicted representations. Moreover, if the correct filter lies within the space of filters being explored, maximizing mutual information becomes equivalent to simultaneously maximizing every dependence measure that satisfies the Data Processing Inequality. It is important to note that maximizing mutual information will typically leave a small number of directions in parameter space unconstrained. We term these directions "diffeomorphic modes" and present an equation that allows these modes to be derived systematically. The presence of diffeomorphic modes reflects a fundamental and nontrivial substructure within parameter space, one that is obscured by standard likelihood-based inference.