In hidden Markov chains, the system's behavior depends on latent (or hidden) variables. This has a lot of applications in contemporary AI. For now, focus on grasping the high-level themes and ideas. If the subject interests you, you can dive deeper into technical details. The examples are particularly instructive.
Let and be discrete-time stochastic processes and . The pair is a hidden Markov model if
Let and be continuous-time stochastic processes. The pair is a hidden Markov model if
The states of the process (resp. are called hidden states, and (resp. is called emission probability or output probability.