Analyze Hidden Markov Models with Viterbi, Forward, Forward-Backward, and Baum-Welch algorithms.
npx skills add https://github.com/GuitarAlchemist/ix --skill ix-hmm
Please help me install this Agent Skill. Skill: ix-hmm Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-hmm Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-hmm
This Skill provides tools for analyzing Hidden Markov Models, enabling users to decode hidden state sequences, learn model parameters, and compute state probabilities from sequential observations.
Analyze a hidden Markov model using the ix-hmm skill with the Viterbi algorithm on the provided observations.
High-intent search queries and answers about installing and using this skill.
To decode hidden state sequences from observations in a Hidden Markov Model, use the Viterbi algorithm. It calculates the maximum probability path through the states, providing the optimal sequence for your sequential data.
The Baum-Welch algorithm learns Hidden Markov Model parameters by iteratively adjusting transition and emission probabilities. It uses the Forward-Backward algorithm to maximize the likelihood of the observed sequential data.
Yes, the Forward-Backward algorithm computes posterior state probabilities for sequential data. It calculates the probability of being in a specific state at each time step, given the entire observation sequence.
Yes, Hidden Markov Model analysis requires specific dependencies. You must have the ix_graph dependency installed, along with the necessary Rust library dependencies to execute the scripts.
The best way to calculate the total probability of an observation sequence in a Hidden Markov Model is using the Forward algorithm. It efficiently sums the probabilities across all potential hidden state paths.
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