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Bayesian econometrics and forecasting

机译:贝叶斯计量经济学与预测

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摘要

Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferences laid down in the mid-twentieth century, and utilize numerical methods developed since that time in their implementation. These methods unify the tasks of forecasting and model evaluation. They also provide tractable solutions for problems that prove difficult when approached using non-Bayesian methods. These advantages arise from the fact that the conditioning in Bayesian probability forecasting is the same as the conditioning in the underlying decision problems.
机译:当代贝叶斯预测方法基于20世纪中叶奠定的主观概率和偏好基础,并利用自那时以来开发的数值方法。这些方法统一了预测和模型评估的任务。它们还为使用非贝叶斯方法难以解决的问题提供了易于解决的解决方案。这些优点来自以下事实:贝叶斯概率预测中的条件与潜在决策问题中的条件相同。

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