Hidden Markov Models
Also known as HMM
Overview
Hidden Markov Models (HMMs) infer unobserved states from sequences of observations, making them useful when regimes such as disease stages, market conditions, or behaviors cannot be measured directly. Core methods include Baum–Welch for parameter estimation, Viterbi decoding for the most likely state sequence, and filtering for online predictions; factorial and Bayesian variants handle structured or uncertain dynamics. Current work combines HMMs with neural models, reinforcement learning, and richer emission distributions, while developing faster inference for large state spaces and streaming data. A central tension is balancing accurate, adaptable predictions with interpretable states, computational efficiency, and reliable inference under noise or model misspecification.
Latest papers
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