Modelo de Markov Oculto (HMM)
Um Oculto Modelo de Markov (HMM) is a powerful statistical tool used in various fields, including inteligência artificial, reconhecimento de fala, 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 produz eventos observáveis com base em probabilidades de emissão específicas.
As principais características dos HMMs incluem:
- Estados: The underlying states of the system, which are not directly observable but can be inferred from the dados observados.
- Observações: The events or outputs that can be seen and measured, which provide clues about the hidden states.
- Probabilidades de Transição: The probabilities of moving from one hidden state to another, which inform how the system evolves over time.
- Probabilidades de Emissão: As probabilidades de observar certos eventos dado um 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 processamento de linguagem natural, where they help in part-of-speech tagging, and in finance for modeling stock prices.