Hidden Markov Model

A Hidden Markov Model (HMM) is a statistical framework used to describe systems where the internal state is not directly observable (hidden) but can be inferred through observations. Think of it like a person walking through a fog: you can't see where they are exactly, but you can tell their movement by the sounds they make. HMMs use probabilities to predict the likelihood of different states based on observed data, enabling applications in fields like speech recognition, bioinformatics, and finance, where the underlying processes are complex and hidden from direct view.