Model Calibration with Markov Chain Monte Carlo Tutorial

The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.

本教程旨在演示如何使用马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）方法完成模型校准。所谓校准，指的是选取模型参数（在相关场景下还包括模型结构）。模型开发与诊断中的一项常见目标是校准，即识别出与数据相符的模型结构与参数。尽管可通过手动调整参数或最小化诸如均方根误差（root-mean-square-error, RMSE）这类简单误差指标来完成模型校准，但此类方法无法充分刻画数据生成过程的概率属性，也未能涵盖多种模型配置与数据相符的潜在可能性。本教程所聚焦的概率不确定性量化即可解决上述问题。本教程作为电子书《多部门动力学研究中的不确定性处理》的附录呈现。