Fit Hidden Markov Model
技能 已验证 活跃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.
To provide a precise and robust implementation for analyzing time series data generated by unobservable latent states.
功能
- Fit HMMs using Baum-Welch (EM) algorithm
- Perform Viterbi decoding for most likely state sequence
- Implement model selection using BIC/AIC
- Handle Gaussian, discrete, Poisson, and multinomial emissions
- Support multiple restarts to avoid local optima
使用场景
- Segment time series into latent regimes (e.g., market regimes, speech phonemes)
- Compute probabilities of observed sequences
- Decode the most likely hidden state path
- Compare models with different numbers of hidden states
非目标
- Handling of non-sequential data
- Models with infinite hidden states
- Real-time streaming data processing
- Advanced state-dwell time modeling (e.g., HMM-MARKOV)
工作流
- Define Hidden States and Observation Model
- Initialize Parameters
- Run Baum-Welch EM for Parameter Estimation
- Apply Viterbi Decoding for Most Likely State Sequence
- Perform Model Selection (BIC/AIC Across Model Orders)
- Validate with Held-Out Data and Posterior Decoding
实践
- Statistical modeling
- Time series analysis
- Machine learning
- Algorithm implementation
先决条件
- Observations sequence/matrix
- Number of hidden states (integer or range)
- Emission type string (gaussian, discrete, poisson, multinomial)
Practical Utility
- info:Usage examplesWhile the SKILL.md provides detailed inputs and a procedure, concrete, ready-to-run examples showing input, invocation, and output are missing.
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
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