Comments (5)
Set optimizer=None in https://github.com/probml/ssm-jax/blob/main/ssm_jax/hmm/models/bernoulli_hmm.py#L68
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Here are the details from my book 1. Note that the add one smoothng prior corresponds to Beta(2,2) which is much stronger than your current Beta(1.1, 1.1). This might explain your numerical problems.
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Replace beta.mode in https://github.com/probml/ssm-jax/blob/main/ssm_jax/hmm/models/bernoulli_hmm.py#L99 with pseudo counts
I actually like the Beta(count1, count0).mode()
formulation! I think it shows off the general form of the M-steps.
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I think we've all settled on using the Beta(count1, count0).mode()
. Could I close this issue?
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Yup!
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