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name: probabilistic-and-sequence-models description: "Routes numpy-ml probabilistic, topic-model, hidden-Markov, and n-gram tasks with corpus and probability-shape guidance." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Probabilistic and Sequence Models

Use this sub-skill for numpy-ml workflows around probability models, hidden state models, topic models, and sequence language models:

• Gaussian mixture models;
• multinomial hidden Markov models;
• LDA and smoothed LDA;
• maximum-likelihood, additive, and Good-Turing n-gram language models.

First Checks

1. Read references/api-reference.md for exact constructor signatures and the methods that expect arrays versus corpus file paths.

2. Run the smoke script for tiny validation:

   python sub-skills/probabilistic-and-sequence-models/scripts/probabilistic_sequence_smoke.py
   

3. Read references/workflows.md for small end-to-end examples and when to combine this route with preprocessing.

4. Read references/troubleshooting.md when probability matrices, stochasticity checks, or corpus/tokenization inputs are wrong.

Route by Task

User asks for	Use this route
Gaussian mixture clustering or density fitting	GMM workflow and tiny synthetic arrays.
HMM log likelihood or decoding	HMM workflow with integer observation sequences and valid matrices.
LDA topic modeling	LDA/SmoothedLDA workflow with document-term style inputs.
n-gram scoring or corpus language modeling	n-gram workflow with a text corpus file and tokenization choices.
tokenization, stop words, punctuation cleanup	Cross-link to ../preprocessing-and-utilities/SKILL.md first.

Operating Notes

• fit and related training calls mutate the model in place in the usual numpy-ml style.
• HMM/GMM/LDA/n-gram examples in the repo are intentionally small; prefer bounded arrays or tiny text corpora when validating behavior.
• Do not require plot scripts or notebook-style demos for routine use.