Data Science & Analytics

Fit Hidden Markov Model

Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

Publisher / source

pjt222/agent-almanac (found on the CSV list)

Upstream record

pjt222/agent-almanac

Version

Version 1

Published

2026-11-03

Publication review

Approved for publication · recorded passed scan 2026-11-03

Content fingerprint

6304fbdca9317df0fe1f091d1eddc3e3068fa2715934a3ff30d248aa864e6063