In this work, we review the theory involved in the Bayesian calibration of complex computer models, with particular emphasis on their use for applications involving computationally expensive simulations and scarce experimental data. In the article, we present a unified framework that incorporates various Bayesian calibration methods, including well-established approaches. Furthermore, we describe their implementation and use with a new, open-source Python library, ACBICI (A Configurable BayesIan Calibration and Inference Package). All algorithms are implemented with an object-oriented structure designed to be both easy to use and readily extensible. In particular, single-output and multiple-output calibration are addressed in a consistent manner. The article completes the theory and its implementation with practical recommendations for calibrating the problems of interest. These guidelines -- currently unavailable in a unified form elsewhere -- together with the open-source Python library, are intended to support the reliable calibration of computational codes and models commonly used in engineering and related fields. Overall, this work aims to serve both as a comprehensive review of the statistical foundations and (computational) tools required to perform such calculations, and as a practical guide to Bayesian calibration with modern software tools.

翻译：本文系统回顾了复杂计算机模型贝叶斯校准的理论体系，特别关注其在计算成本高昂的仿真与实验数据稀缺场景中的应用。我们提出了一个统一框架，该框架整合了多种贝叶斯校准方法，包括成熟的主流方法。此外，我们通过新型开源Python库ACBICI（可配置贝叶斯校准与推理工具包）阐述了这些方法的实现与应用。所有算法均采用面向对象结构实现，兼具易用性与可扩展性。特别地，单输出与多输出校准问题在框架中得到了统一处理。本文在理论阐述与实现方案的基础上，进一步提供了针对实际校准问题的实用建议。这些目前尚未以统一形式公开的指导原则，结合开源Python库，旨在为工程及相关领域常用计算代码与模型提供可靠的校准支持。总体而言，本研究既是对相关计算所需统计学基础与（计算）工具的全面综述，也可作为利用现代软件工具进行贝叶斯校准的实践指南。