Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models

作者：Mattila Robert^*; Rojas Cristian R; Krishnamurthy Vikram; Wahlberg Bo

来源：IEEE Signal Processing Letters, 2017, 24(12): 1813-1817.

DOI：10.1109/LSP.2017.2759902

• Hidden Markov models (HMM)
• maximum-likelihood (ML)
• method of moments
• system identification

We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. We propose 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, first, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and second, to numerically show that the method is computationally less demanding than conventional methods-in particular for large datasets.

• 出版日期2017-12