I am trying to more deeply understand the difference between Hidden Markov Models and Bayesian Network? The general idea is that HMMs have a single variable which has probabilities of entering different states, known and unknown, whereas the Bayesian network relates multiple random variables.
So, given that, what would examples of particular problems that would be appropriate for each?