A Hidden Markov Model (HMM) is a statistical model that represents systems with hidden states, where the system transitions between these states over time and generates observable outputs. HMMs are particularly useful for modeling time series data where the underlying process is not directly observable, allowing us to infer hidden states based on observed data. They play a key role in various applications such as speech recognition, bioinformatics, and financial modeling by leveraging probabilistic transitions and emissions to capture complex temporal patterns.
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