hidden markov models
Hidden Markov Models (HMMs) are statistical models used to represent systems that have both observed and unobserved states. They consist of two main components: a set of hidden states and a set of observed states. The hidden states represent the underlying processes or phenomena that are not directly observable, while the observed states correspond to the data or measurements that are observed. HMMs are characterized by their probabilistic nature, as the transitions between hidden states and the emissions of observed states are governed by probabilities. The transitions between hidden states are modeled using transition probabilities, which indicate the likelihood of moving from one state to another. The emissions of observed states are modeled using emission probabilities, which indicate the likelihood of observing a particular state given the current hidden state. HMMs are widely used in various fields, including speech recognition, natural language processing, bioinformatics, and finance. They are particularly useful for problems involving sequential data, where the current state depends on the previous states and influences the future states. By leveraging the concept of hidden states, HMMs can capture the underlying structure and dynamics of a system, providing valuable insights and predictions. In summary, hidden Markov models are probabilistic models that capture the relationships between observed and unobserved states in systems with sequential data. They are widely used in various fields to analyze and predict patterns in data.
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