In binary logistic temporal-graph models, higher Fisher information for parameter recovery coincides with higher irreducible predictive entropy, so the easiest-to-estimate regimes are the hardest to predict.
arXiv preprint arXiv:2506.12588 , year=
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Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs
In binary logistic temporal-graph models, higher Fisher information for parameter recovery coincides with higher irreducible predictive entropy, so the easiest-to-estimate regimes are the hardest to predict.