This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particularly useful for a range of problems in this domain, such as the covering technique, the Hanson-Wright Inequality and the method of self-normalized martingales. We then employ these tools to give streamlined proofs of the performance of various least-squares based estimators for identifying the parameters in autoregressive models. We conclude by sketching out how the ideas presented herein can be extended to certain nonlinear identification problems.

翻译：本教程旨在介绍系统辨识理论中近期发展的非渐近方法，主要聚焦于线性系统。我们重点阐述在该领域一系列问题中具有特殊效用的工具，例如覆盖技术、汉森-怀特不等式以及自归一化鞅方法。随后运用这些工具，对流线型证明自回归模型参数辨识中各类最小二乘估计器的性能进行论证。最后概要说明如何将所述思想拓展至特定非线性辨识问题。