Comments (5)
Yes, I've been using SLDS on a list of recordings. You just have to pass a list of time series instead of a single one to the functions. The list elements have to be in the shape of T x D
where T is the number of time points (the different time series can have different lengths) and D is the dimensionality of your observations.
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Thanks @bagibence, that's exactly right.
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@bagibence That is good news! I am going to try it right now! Thanks for the prompt reply!
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@slinderman, just one doubt... the fact that we see the ELBO not having a monotonically increasing behavior comes from the fact you are using the stochastic version for the mean field variational inference, right?
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The ELBO should monotonically increase for HMMs fit with EM. We've implemented exact M-steps for most observation models. For SLDS, the examples are currently using black box variational inference with SGD, Adam, rmsprop, etc. We've implemented a few variational families including mean field
q(x) = \prod_t N(x_t | mu_t, Sigma_t)
and a structured variational posterior
q(x) = N(x | mu, Sigma)
where mu
is a (TD,) vector and Sigma
is a (TD x TD) matrix with a block-tridiagonal inverse. I.e. the variational posterior corresponds to a chain structured graph, as in an LDS.
In a separate branch, David Z. and I are working on a Laplace variational inference method that maintains chain-structured posteriors on both q(x) and q(z), and is much more efficient than BBVI.
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Related Issues (20)
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