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，主要关注线性系统辨识。我们重点阐述了在该领域一系列问题中尤为实用的工具，包括覆盖技术、汉森-赖特不等式以及自归一化鞅方法。随后，我们运用这些工具，为基于最小二乘法的各类估计器在自回归模型参数辨识中的性能提供了简洁的证明。最后，我们概述了如何将本文提出的思想扩展到某些非线性辨识问题。