已知传感器隐马尔可夫模型的渐进高效识别
Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
AI总结：
本文提出一种两步算法，通过矩估计和牛顿-拉夫森最大似然估计，理论证明其一致性与渐进效率，并在大规模数据集上展示计算效率优势。
摘要：
我们考虑估计具有已知观测概率的有限状态有限观测字母隐马尔可夫模型的转移概率矩阵。主要贡献是一种两步算法：矩估计（形式化为凸优化问题）后跟一次牛顿-拉夫森最大似然估计。本文的双重贡献是，首先理论证明所提估计器的一致性和渐进效率，其次数值显示该方法在大规模数据集上计算效率优于传统方法。
英文摘要：
We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. The main contribution is a two-step algorithm; a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum likelihood estimator. The two-fold contribution of this letter is, firstly, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and secondly, to numerically show that the method is computationally less demanding than conventional methods - in particular for large data sets.
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。
