So far, all standard HMM implementations I've seen assume some variation of a Gaussian Mixture (GMM) as their emission model. It can of course only have a single mixture component which reduces it to a standard multivariate normal distribution.

In other words, conditional on the hidden state, a particular GMM model produces the observations.

Is it possible to replace this GMM with an autoregressive model, for example, a Vector Autoregression (VAR) in the multivariate case? And if yes, how would the parameters of this model be updated within the Baum-Welch forward/backward parameter estimation framework?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.