The state of the process in HMM’s model is described by a ‘Single Discrete Random Variable’.
In a Hidden Markov Model (HMM), the state of the process is typically described probabilistically. Specifically, it is represented as a set of hidden states, each with associated probabilities. These hidden states represent the underlying, unobservable processes that generate observed data.
The state of the process at any given time is described by a probability distribution over the set of possible states. This distribution captures the uncertainty about which state the process is in at that time step. In a discrete HMM, this probability distribution is often represented using a probability vector where each element corresponds to the probability of being in a particular state.
In summary, the state of the process in an HMM is described probabilistically, reflecting the uncertainty inherent in the system being modeled.