Observable operator models (OOMs) offer a powerful framework for modelling stochastic processes, surpassing the traditional hidden Markov models (HMMs) in generality and efficiency. However, using OOMs to model infinite-dimensional processes poses significant theoretical challenges. This article explores a rigorous approach to developing an approximation theory for OOMs of infinite-dimensional processes. Building upon foundational work outlined in an unpublished tutorial [Jae98], an inner product structure on the space of future distributions is rigorously established and the continuity of observable operators with respect to the associated 2-norm is proven. The original theorem proven in this thesis describes a fundamental obstacle in making an infinite-dimensional space of future distributions into a Hilbert space. The presented findings lay the groundwork for future research in approximating observable operators of infinite-dimensional processes, while a remedy to the encountered obstacle is suggested.

使用可观测算子模型（OOMs）对无限维过程建立近似理论, 探索了未来分布空间的内积结构以及可观测算子相对于关联2范数的连续性, 并描述了将无限维未来分布空间变成希尔伯特空间的基本障碍，为无限维过程的可观测算子近似提供了基础并提出了解决方法。

可观察算子模型的逼近理论