 Hidden Markov model

A hidden Markov model is a Markov process whose state is latent and observed only through random emissions whose conditional distribution depends on the current hidden state.

A Hidden Markov Model (HMM) is a statistical model that is used to describe systems that are assumed to be a Markov process with hidden states. It is particularly useful in fields such as speech recognition, bioinformatics, and time series analysis. Here are the key components and concepts associated with HMMs: ### Key Components 1. **States**: HMMs consist of a set of hidden states.