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About this paper
Abhinav Singh, Carsten Wiuf, Abhishek Behera, Manoj Gopalkrishnan
The authors present a chemical reaction network for learning hidden Markov model parameters from ordered observations. It passes information forward and backward along the sequence, avoiding separate hidden-state-path representations and giving network size linear in sequence length for fixed state and symbol sets. In deterministic simulation, endpoint estimates matched Baum–Welch on a 25-symbol sequence, but a boundary example differed.
Summary by alphaXiv.
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