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This repository has been archived by the owner on Sep 11, 2023. It is now read-only.
In pyemma/msm/estimators/bayesian_hmsm.py, the class BayesianHMS allows a prior to be input for the transmission matrix but not for the emission matrix. This limits the ways that other information can be brought in to inform the estimation. Although this could, apparently, be carried out using lower-level routines, it takes a rather high level of familiarity with the package to figure this out. So for the convenience of end-users, it would be great if this prior could also be included in the constructor for BayesianHMS.
Alternatively (or additionally?), all the priors could be included as input parameters for the _estimate method. This would allow one BayesianHMS to be used on multiple priors as well as multiple data sets. I'm not sure how much overhead is involved in creating BayesianHMS - if it is not much, then this is perhaps not worth the trouble.
The text was updated successfully, but these errors were encountered:
Is there anyway that we can help with this issue? We have a drop-dead deadline of end of November to submit a journal paper that depends on the use of this functionality, and I am concerned that we will not have enough time to test our implementation and prepare this paper.
In pyemma/msm/estimators/bayesian_hmsm.py, the class BayesianHMS allows a prior to be input for the transmission matrix but not for the emission matrix. This limits the ways that other information can be brought in to inform the estimation. Although this could, apparently, be carried out using lower-level routines, it takes a rather high level of familiarity with the package to figure this out. So for the convenience of end-users, it would be great if this prior could also be included in the constructor for BayesianHMS.
Alternatively (or additionally?), all the priors could be included as input parameters for the _estimate method. This would allow one BayesianHMS to be used on multiple priors as well as multiple data sets. I'm not sure how much overhead is involved in creating BayesianHMS - if it is not much, then this is perhaps not worth the trouble.
The text was updated successfully, but these errors were encountered: