The Interactive Hidden Markov Model with real Practical applied

Abstract

I proposed an Interactive Hidden Markov Model (IHMM) where the transitions of hidden states depend on the current observable states. The IHHM is a generalization of the HMM. I note that this kind of HMM is different from classical HMMs where the next hidden states are governed by the previous hidden states only. An example is given to demonstrate IHMM. I'll extend the results to give a general IHMM.