Biases in Inverse Ising Estimates of Near-Critical Behaviour
This addresses a problem for researchers using inverse Ising inference in fields like neuroscience, highlighting incremental improvements in bias correction.
The study tackled biases in inverse Ising estimates, showing that methods like pseudo-likelihood maximization produce large biases near critical regimes, making inferred models appear closer to criticality than warranted, with data-driven corrections applied to an fMRI dataset.
Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as Pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick (SK) model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer-to-criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging (fMRI) dataset from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world datasets.