Modèle de Markov Caché (HMM)
Un Caché Modèle de Markov (HMM) is a powerful statistical tool used in various fields, including intelligence artificielle, reconnaissance vocale, and bioinformatics. It is particularly useful for modeling systems that exhibit a sequence of observable events influenced by internal states that are not directly visible (hence ‘hidden’).
At its core, an HMM consists of two main components: a set of hidden states and a set of observable events. The model assumes that the system transitions between these hidden states according to certain probabilities, and each état caché produit des événements observables en fonction de probabilités d'émission spécifiques.
Les caractéristiques clés des HMM incluent :
- États : The underlying states of the system, which are not directly observable but can be inferred from the données observées.
- Observations : The events or outputs that can be seen and measured, which provide clues about the hidden states.
- Probabilités de Transition : The probabilities of moving from one hidden state to another, which inform how the system evolves over time.
- Probabilités d'Émission : Les probabilités d'observer certains événements en fonction d'un état caché spécifique.
HMMs are commonly trained using algorithms such as the Baum-Welch algorithm or the Viterbi algorithm, which help estimate the model parameters and find the most likely sequence of hidden states given the observed data. Applications of HMMs span across various domains, including traitement du langage naturel, where they help in part-of-speech tagging, and in finance for modeling stock prices.