Abstract Often in Bayesian anlysis closed-form posteriors cannot be derived for complex models. However, it is important to be able to do Bayesian analysis relatively easily. This article presents an alternative, the more general Markov chain Monte Carlo (MCMC) simulation approach, which permits the efficient development of posterior distributions. MCMC simulation methods are now becoming the state of the art in numerous empirical and analytical applications in applied mathematics, biostatistics, marketing, economics, and other areas, but those methods are noticeably absent in the engineering economic analysis literature. The purpose of this article is to introduce MCMC simulation methods to the engineering economics research and practitioner community. Using postaudits and cost estimation as application areas, the article focuses on what MCMC simulation entails, its advantages, and its disadvantages and highlights the usefulness and versatility of the approach.

中文翻译：

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审计后和成本估算应用：贝叶斯回归分析的 MCMC 模拟说明

摘要 在贝叶斯分析中，通常无法为复杂模型导出封闭形式的后验。然而，能够相对容易地进行贝叶斯分析是很重要的。本文提出了另一种更通用的马尔可夫链蒙特卡罗 (MCMC) 模拟方法，它允许有效地开发后验分布。MCMC 模拟方法现在正在成为应用数学、生物统计学、市场营销、经济学和其他领域众多实证和分析应用的最新技术，但这些方法在工程经济分析文献中明显缺失。本文的目的是向工程经济学研究和从业者社区介绍 MCMC 模拟方法。使用后期审计和成本估算作为应用领域，