Comments (2)
helske
commented on May 29, 2024
In MHMM, we have a mixture of multiple HMM's (soft "clusters"), but there is no concept of clusters for single HMM. You can get the probabilities of starting from certain hidden states by looking at the estimated initial probabilities (model$initial_probs) or individual-specific posterior probabilities of being in a certain state at a certain time posterior_probs(model),
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loxavia
commented on May 29, 2024
Thank you very much, this is incredibly useful, been searching for these for a while. Great work !
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Related Issues (20)
- [Help request] How to test the model? HOT 1
- can this package support Multivariate Discrete HMM?? HOT 3
- will it work for multivariate time series prediction : different continues or/and discrete/category observation HOT 4
- seqdata should be a state sequence object HOT 1
- Apply to financial time series? HOT 1
- Parsing HMMER3 files HOT 2
- 'System seems singular' and 'EM algorithm failed' for Mixture Markov Model HOT 2
- Building a model with different sequence lengths HOT 1
- hidden_paths does not respect sequence length HOT 4
- Parallel computation HOT 2
- Forward probability of MHMM vs HMM HOT 1
- standard errors for HMM parameters HOT 3
- Maximum number of colours in cpal/colorpalette HOT 4
- Extracting combinations of emitted states HOT 4
- Error in if (em.con$reltol < resEM$change) { : argument is of length zero HOT 5
- Absorbing state broken in `build_mm()` (seqhmm 1.2.1-1) HOT 1
- Runtime Estimation HOT 1
- EM algorithm failed HOT 1
- Error: number of labels must equal number of states in the alphabet HOT 4
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