Hidden Markov Models
Hidden Markov Models (HMMs) are statistical models used to describe systems that transition between hidden states over time, where only the observable outputs (emissions) are visible. They are based on Markov processes, assuming the future state depends only on the current state, not the past. HMMs are widely applied in fields like speech recognition, bioinformatics, and natural language processing for tasks such as sequence labeling and pattern recognition.
Developers should learn HMMs when working on problems involving sequential data with hidden underlying states, 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 where the true state is not directly observable, enabling probabilistic inference and prediction in applications like speech-to-text systems or financial forecasting.