经济学与金融学中的贝叶斯预测：现代综述
Bayesian Forecasting in Economics and Finance: A Modern Review
AI总结：
本综述系统回顾了贝叶斯统计范式在经济学与金融学概率预测中的应用，阐述了如何通过积分量化未知量不确定性，并介绍了现代贝叶斯计算方法及实施细节。
摘要：
贝叶斯统计范式为概率预测提供了一种有原则且连贯的方法。任何预测问题中所有未知量（模型、参数、潜在状态）的不确定性都能被明确量化，并通过积分或平均过程纳入预测分布。伴随方法的优雅性，贝叶斯预测如今受到蓬勃发展的贝叶斯计算领域的支撑，使得几乎能为任何规模或复杂程度的问题生成贝叶斯预测。经济学与金融学中贝叶斯预测的现状是本综述的主题。旨在为读者提供该领域现代方法的概述，并置于一定的历史背景中；同时提供充足的计算细节以协助读者进行实施。
英文摘要：
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
