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Researchers analyze computational complexity of Hidden Markov Model identification

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed the computational complexity of identifying Hidden Markov Models (HMMs). They developed algorithms to determine if a given HMM is identifiable, a crucial step for recovering model parameters from data. The study shows that various identifiability problems for HMMs are decidable within PSPACE, with deterministic variants being coETR-hard. AI

RANK_REASON The item is an academic paper detailing theoretical research on computational complexity. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers analyze computational complexity of Hidden Markov Model identification

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The item is an academic paper detailing theoretical research on computational complexity. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Markel Zubia, Nils Jansen ·

    On the Computational Complexity of Hidden Markov Model Identification

    arXiv:2610.09104v1 Announce Type: cross Abstract: Identification is the task of recovering the parameters of an unknown ground-truth model from sampled data. When parameters other than the ground truth induce the same output distribution, data alone does not provide enough inform…

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