Beam-Limited k-Step Lookahead for Computationally Efficient HMM Decoding

Authors : Mehmet Kurucan

Pages : 545-558

Doi:10.21605/cukurovaumfd.1708178

View : 29 | Download : 44

Publication Date : 2025-09-26

Article Type : Research Paper

Abstract :Hidden Markov Models (HMMs) are widely used in many sequential decision-making problems due to their ability to model time-related dependencies. The standard decoding methods in these models, such as the Viterbi algorithm, are limited by their dependence on past observations only. Thus, this leads to unpredictability when future information is available. In this work, we propose a decoding strategy called Beam-Limited k-Step Lookahead that looks k-step ahead, drawing parallels to k-step discrete control synthesis, to make use of future information. The proposed method achieves a balance between decoding accuracy and computational complexity by constraining the search space to the top M most promising paths. Experimental results on synthetic HMM data show that our new decoding strategy significantly improves decoding accuracy over classical Viterbi decoding. The findings highlight the potential of this new strategy to improve the performance of sequential decoding systems.
Keywords : Saklı Markov Modelleri, Kod Çözme Problemleri, Sınırlı Işın Araması, Ardışık Tahminleme

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