Modelo de Markov Oculto (HMM)
Un Oculto Modelo de Markov (HMM) is a powerful statistical tool used in various fields, including inteligencia artificial, reconocimiento de voz, 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 estado oculto produce eventos observables basados en probabilidades de emisión específicas.
Las características clave de los HMMs incluyen:
- Estados: The underlying states of the system, which are not directly observable but can be inferred from the datos observados.
- Observaciones: The events or outputs that can be seen and measured, which provide clues about the hidden states.
- Probabilidades de Transición: The probabilities of moving from one hidden state to another, which inform how the system evolves over time.
- Probabilidades de Emisión: Las probabilidades de observar ciertos eventos dado un estado oculto específico.
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 procesamiento de lenguaje natural, where they help in part-of-speech tagging, and in finance for modeling stock prices.