Estimação paramétrica e não-paramétrica em modelos de markov ocultos
In this work we study the Hidden Markov Models with finite as well as general state space. In the finite case, the forward and backward algorithms are considered and the probability of a given observed sequence is computed. Next, we use the EM algorithm to estimate the model parameters. In the gener...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | Brasil |
| Recursos: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/18630 |
| Acesso em linha: | https://repositorio.ufrn.br/jspui/handle/123456789/18630 |
| Access Level: | acceso abierto |
| Palavra-chave: | Cadeia de markov Modelos de markov oculto . Espaço de estados finito Espaçco de estados geral Markov chain Hidden markov models Finite state space General state space CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA::MATEMATICA APLICADA |
| Resumo: | In this work we study the Hidden Markov Models with finite as well as general state space. In the finite case, the forward and backward algorithms are considered and the probability of a given observed sequence is computed. Next, we use the EM algorithm to estimate the model parameters. In the general case, the kernel estimators are used and to built a sequence of estimators that converge in L1-norm to the density function of the observable process |
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