Hidden Markov Model
A Hidden Markov Model (HMM) is a statistical model used to describe systems where the underlying states are not directly observable but can be inferred from observable outputs. It consists of a Markov process with hidden states and observable emissions, where each state has a probability distribution over possible outputs. HMMs are widely applied in fields like speech recognition, bioinformatics, and natural language processing for sequence analysis and pattern recognition.
Developers should learn HMMs when working on problems involving sequential data where the true state is hidden, such as part-of-speech tagging in NLP, gene prediction in genomics, or gesture recognition in computer vision. They are particularly useful for modeling time-series data with probabilistic transitions and emissions, enabling tasks like prediction, classification, and decoding of sequences in machine learning and AI applications.