Hidden Markov Models

Hidden Markov Models (HMMs) are statistical tools used to analyze data where the system being studied moves between different states that are not directly observable (hidden). Instead, we observe related signals or outputs influenced by these states. The model predicts the sequence of hidden states based on observed data, assuming the system transitions from one state to another in a probabilistic way, and each state produces observable outputs with certain likelihoods. HMMs are widely used in areas like speech recognition, where they help interpret sequences of sounds to understand spoken words.