Data Science Asked on December 29, 2020
The original HMM model looks only one element back (considers only previous state). Sometimes it viable to look two elements back. Is there an extension to HMM that allows that?
I think the keyword you are looking for is Higher Order Hidden Markov Models. Your "ordinary" HMM would be considered a First Order HMM. So yes, there is literature these types of HMMs.
Answered by KyuMirthu on December 29, 2020
An extension of Bayesian Network, Dynamic Bayesian Network (DBN) and 2-TBN in particular (looking at 2 time points at a time), or X-TBN (if you wish) can be useful here.
A node in such DBN would be a probability variable that represents the current state of an HMM represented by that node. Specific structure of DBN is up to you, you can for example incorporate different levels of HHMM (hierarchical HMM) into that DBN which also contains nodes that represent t-1 points in time.
Answered by Sergey Shcherbakov on December 29, 2020
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