Comments (3)
hi @slinderman !
Any progress on the above, would be awesome addition to this great library.
Best,
Andrew
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Hi @andrewczgithub, I think the current implementation should work just fine, but there are some small changes that could further improve the estimation. @ahwillia has been thinking about this too. The next step is to write up a short derivation of the M-step for autoregressive models with correlated noise. We need to do this anyway for the LDS models, since there we want to maximize the expected log probability under the posterior distribution on continuous latent states.
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Update: Spoke with @ahwillia and we convinced ourselves that for the M-step of linear regressions (including linear autoregressive models), the MLE mean estimate does not depend on the covariance, so our two-step procedure should be valid.
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Related Issues (20)
- State Transition Parameters HOT 1
- using GPU HOT 1
- documentation: add pointer to ssm-jax
- access lag-specific autoregressive A matrix HOT 1
- Regularization of GLM weights for input driven observations and transitions HOT 1
- Singularity Container for the code
- error during installation HOT 4
- ARHMM usage HOT 1
- Unit tests fail in Python 3.8 HOT 1
- Diagonal Gaussian emissions for LDS? HOT 1
- Constraining AR-HMM emission parameters HOT 4
- Explained variance for the rSLDS latents HOT 3
- How to use _vjp_solve_banded_A HOT 1
- Is there any documentation for the ssm package, or at least for GLM-HMM part? HOT 1
- Time-varying C matrix? HOT 1
- Finding the variance explained for # of dimensions HOT 4
- Question on method ssm.HMM (GLM weight sign) HOT 2
- Trouble with SSM installation HOT 7
- rSLDS fitting changes significantly each time I initializa HOT 2
- Question about alpha calculation in forward pass HOT 1
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