摘要翻译：
在不精确隐马尔可夫模型(iHMM)中，我们提出了一种从输出（或观测）估计状态序列的高效精确算法，其中连接一个状态到下一个状态的不确定性和连接一个状态到其输出的不确定性都用相干的下预见性表示。我们与代表iHMM的信度网络相联系的独立性概念是认知无关性的概念。我们考虑后验联合状态模型的（Walley-Sen）最大序列作为状态序列的最佳估计，其条件是观察到的输出序列，并与作为状态序列指示器的增益函数相关联。这对应于（并推广）在具有精确转换和输出概率的HMMs中寻找具有最高后验概率的状态序列。我们认为，计算复杂度与马尔可夫链长度成平方关系，与状态个数成立方关系，与最大状态序列个数成线性关系。对于二元iHMMs，我们通过实验研究了最大状态序列的个数如何依赖于模型参数。我们还给出了一个简单的玩具在光学字符识别中的应用，证明了我们的算法可以用来鲁棒化由精确概率模型做出的推理。
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英文标题：
《An efficient algorithm for estimating state sequences in imprecise
  hidden Markov models》
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作者：
Jasper De Bock and Gert de Cooman
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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英文摘要：
  We present an efficient exact algorithm for estimating state sequences from outputs (or observations) in imprecise hidden Markov models (iHMM), where both the uncertainty linking one state to the next, and that linking a state to its output, are represented using coherent lower previsions. The notion of independence we associate with the credal network representing the iHMM is that of epistemic irrelevance. We consider as best estimates for state sequences the (Walley--Sen) maximal sequences for the posterior joint state model conditioned on the observed output sequence, associated with a gain function that is the indicator of the state sequence. This corresponds to (and generalises) finding the state sequence with the highest posterior probability in HMMs with precise transition and output probabilities (pHMMs). We argue that the computational complexity is at worst quadratic in the length of the Markov chain, cubic in the number of states, and essentially linear in the number of maximal state sequences. For binary iHMMs, we investigate experimentally how the number of maximal state sequences depends on the model parameters. We also present a simple toy application in optical character recognition, demonstrating that our algorithm can be used to robustify the inferences made by precise probability models.
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PDF链接：
https://arxiv.org/pdf/1210.1791