ix-hmm
OfficialAnalyze Hidden Markov Models for sequential data
AuthorGuitarAlchemist
Version1.0.0
Installs0
System Documentation
What problem does it solve?
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.
Core Features & Use Cases
- Viterbi Algorithm: Determines the most likely sequence of hidden states given observations.
- Forward Algorithm: Calculates the total probability of an observation sequence.
- Forward-Backward Algorithm: Computes the posterior probabilities of each state at each time step.
- Baum-Welch Algorithm: Learns the parameters of an HMM from observed data.
- Use Case: Ideal for applications like speech recognition, NLP tagging, biological sequence analysis, and regime detection in finance.
Quick Start
Analyze a hidden Markov model using the ix-hmm skill with the Viterbi algorithm on the provided observations.
Dependency Matrix
Required Modules
ix_graph
Components
scripts
💻 Claude Code Installation
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Please help me install this Skill: Name: ix-hmm Download link: https://github.com/GuitarAlchemist/ix/archive/main.zip#ix-hmm Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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