On the Complexity of Bayesian Signal Processing

Yi Liu

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arXiv · 2608.29840

On the Complexity of Bayesian Signal Processing

Yi Liu

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Abstract

We develop a computational framework for Bayesian decision-making. We show that as long as no action is optimal in every state, Bayes-optimal choice is intractable. This hardness need not arise from large action, state, or signal spaces, nor from a complicated represented utility function: extracting enough information from a hard-to-interpret signal to act optimally can itself be computationally hard. We also characterize tractability across approximation notions and identify their sources of difficulty. Under the probably approximately correct criterion, sample-based Bayesian learning is tractable if and only if the signal support is bounded. Our results provide justifications for bounded rationality, costly Bayesian inference, and sample-based Bayesian learning.

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Yi Liu. 2026-08-30. On the Complexity of Bayesian Signal Processing. https://arxiv.org/abs/2608.29840

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