Hidden Markov Models (HMMs)

Hidden Markov Models (HMMs) model sequential data with a series of hidden states that produce observable outputs. They solve evaluation, decoding, and learning problems for sequences.

Key concept: HMMs have a hidden state process (which follows the Markov property) and an observation process dependent on the current state. Example: Inferring the weather in a windowless room by observing people’s clothing.