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Хлебникова -> Хренникова По поводу трудоноизменяемости (robustness) уже сказали в active inference: variational free energy = complexity — accuracy F[Q, y] = D_KL[Q(x)||P(x)] — E_Q(x)[ln P(y |x)] "The second line emphasizes the interpretation of free energy minimization as finding the best explanation for sensory data, which must be the simplest (minimally complex6) explanation that is able to accurately7 account for the data (cf. Occam’s razor). The complexity-accuracy trade-off recurs across several domains, normally in the context of model comparison for data analysis. In statistics, other approximations to model evidence are sometimes used, such as the Bayesian information criterion or Akaike information criterion. The complexity-accuracy trade-off will become important when we describe how to use free energy for model comparison during model-based data analysis—and in the context of structure learning and model reduction. Inferring explanations that have minimal complexity is also important from a cognitive perspective. This is because one can assume that updating what one knows (the prior) to accommodate the data entails a cognitive cost (Ortega and Braun 2013, Zénon et al. 2019); hence, an explanation that diverges minimally from the prior is preferable. On this view, the complexity cost is just Bayesian surprise. In other words, the degree to which “I change my mind” is quantified by the divergence between the prior and the posterior. This means every accurate explanation for my sensations incurs a complexity cost, and this cost scores the degree of Bayesian belief updating. Variational free energy, then, scores the difference between accuracy and complexity." Отсюда: [https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind](https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind), глава 2 (можно скачать ПДФ), стр. 28-29. Тут complexity и есть robustness.