In this chapter we started with a short recap of 1D HMMs which we introduced in the previous chapter. Later we introduced the concepts of 2D HMMs and derived the various assumptions that we make for 2D HMMs to simplify our computations and it can be applied in image processing tasks. We then introduce a generic EM algorithm for learning the parameters in the case of 2D-HMMs.
In the next chapter, we will look at another application of HMMs in the field of reinforcement learning and will introduce MDP.
