论文标题
不确定性定量和边际MDP模型
Uncertainty Quantification and the Marginal MDP Model
论文作者
论文摘要
本文介绍了Dirichlet过程模型的混合物的新观点，该观点允许恢复与完整模型相关的完整和正确的不确定性量化，即使在整合了随机分布函数之后。这意味着我们可以运行简单的马尔可夫链蒙特卡洛算法，然后返回从集成中删除的原始不确定性。这也具有避免不执行集成步骤的更复杂算法的好处。提出了许多插图。
The paper presents a new perspective on the mixture of Dirichlet process model which allows the recovery of full and correct uncertainty quantification associated with the full model, even after having integrated out the random distribution function. The implication is that we can run a simple Markov chain Monte Carlo algorithm and subsequently return the original uncertainty which was removed from the integration. This also has the benefit of avoiding more complicated algorithms which do not perform the integration step. Numerous illustrations are presented.
