Markov chains (MC)

Markov chains or discrete time Markov Chain(DTMC) are Hopfield networks with memory-less (i.e. Markov Property) nodes and work by state changes from each node to another. They can be thought of as graphs with probabilities that indicate how likely it is that we will move from one point in the chain, a “state”, to another state. Markov chains are used to determine the probability of moving from state j to state i, which can be denoted as p(i,j).
MCs are not neural networks, however they are studies as neural network to form a basis for BMs and HNs. similar to HNs, Markov chains are often fully connected.