为量化动态模型提供统一误差界，连接硬件限制与统计精度。
Uniform error bounds for quantized dynamical models
- 用分块分解和新型间隔点策略，得到两类误差界。
- 误差随编码比特数增长，揭示硬件约束对模型精度的影响。
- 适合系统辨识与混合系统建模研究者参考。
本文为从依赖数据序列中学习的动态模型提供了统计保证。具体而言，我们推导出适用于量化模型及实际系统辨识中常见的不完美优化算法的统一误差界，尤其针对混合系统辨识问题。获得两类误差界：通过分块分解得到慢速率边界，通过新颖的间隔点策略得到快速率、方差自适应边界。这些边界随编码模型所需的比特数而变化，从而将硬件约束转化为可解释的统计复杂度。
原文摘要 · Abstract (English)
This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical contexts for system identification, and in particular hybrid system identification. Two families of bounds are obtained: slow-rate bounds via a block decomposition and fast-rate, variance-adaptive, bounds via a novel spaced-point strategy. The bounds scale with the number of bits required to encode the model and thus translate hardware constraints into interpretable statistical complexities.
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