Hidden Markov Model

Hidden Markov Models (HMMs) are probabilistic models used to analyze sequential data by inferring hidden states from observable outputs. Current research focuses on improving HMM performance and applicability through ensemble methods, coupled models (e.g., combining HMMs with neural networks), and efficient algorithms like Viterbi decoding and variations of the Expectation-Maximization algorithm. HMMs are proving valuable across diverse fields, including natural language processing, finance, bioinformatics, and speech recognition, offering both interpretability and robust performance, particularly in scenarios with limited data or complex dependencies. The development of more efficient algorithms and the integration with other machine learning techniques continue to expand the scope and impact of HMMs.