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A hidden semi-Markov model (HSMM) is a sta … A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed since entry into the current state. This is in contrast to hidden Markov models where there is a constant probability of changing state given survival in the state up to that time. For instance modelled daily rainfall using a hidden semi-Markov model. If the underlying process (e.g. weather system) does not have a geometrically distributed duration, an HSMM may be more appropriate. Hidden semi-Markov models can be used in implementations of statistical parametric speech synthesis to model the probabilities of transitions between different states of encoded speech representations. They are often used along with other tools such artificial neural networks, connecting with other components of a full parametric speech synthesis system to generate the output waveforms. The model was first published by Leonard E. Baum and Ted Petrie in 1966. Statistical inference for hidden semi-Markov models is more difficult than in hidden Markov models, since algorithms like the Baum-Welch algorithm are not directly applicable, and must be adapted requiring more resources. must be adapted requiring more resources.
, (Traducido de https://en.wikipedia.org/wik … (Traducido de https://en.wikipedia.org/wiki/Hidden_semi-Markov_model licencia GFDL y CC-BY-SA 3.0) Un modelo oculto de semi-Markov es un modelo estadístico con la misma estructura que un modelo oculto de Márkov excepto que el proceso inobservable es semi-Márkov en vez de Márkov. Esto significa que la probabilidad de que haya un cambio en el estado oculto depende de la cantidad de tiempo que ha transcurrido desde que entró al estado actual. En contraste a los modelos ocultos de Márkov donde hay una probabilidad constante de cambio de estado dada la supervivencia en el estado hasta ese tiempo. Inferencia estadística para modelos ocultos de semi-Márkov es más difícil que para modelos ocultos de Márkov, ya que algoritmos como Baum-Welch no son aplicables directamente, y deben ser adaptados, lo que requieren más recursos
* Datos: Q3859882 requieren más recursos
* Datos: Q3859882
, Un modello semi-markoviano nascosto (hidde … Un modello semi-markoviano nascosto (hidden semi-Markov model, HsMM) è un processo stocastico che generalizza i modelli di Markov nascosti consentendo che ogni stato della catena di Markov sottostante al processo possa generare una sequenza di osservazioni, anziché una singola osservazione. La "durata" di uno stato non è più quindi unitaria e può distribuirsi secondo una qualsiasi distribuzione di probabilità continua o discreta. I modelli semi-markoviani nascosti consentono di aggirare il vincolo, intrinseco nelle catene di Markov ordinarie, che la durata della permanenza in un certo stato sia distribuita geometricamente. Infatti, in una catena di Markov, la probabilità di rimanere esattamente n unità di tempo nello stesso stato i dipende unicamente dalla probabilità di auto-transizione e, in particolare: In letteratura si trovano numerose applicazioni di questo modello, per esempio in ambito medico, biologico e finanziario.in ambito medico, biologico e finanziario.
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, http://silviac.yolasite.com/resources/UHMSMebook.pdf%7Ctitle=Explicit-duration +
, https://web.archive.org/web/20110617011908/http:/www.nlsde.buaa.edu.cn/~xlliu/icpr2010.pdf +
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A hidden semi-Markov model (HSMM) is a sta … A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed since entry into the current state. This is in contrast to hidden Markov models where there is a constant probability of changing state given survival in the state up to that time. The model was first published by Leonard E. Baum and Ted Petrie in 1966.by Leonard E. Baum and Ted Petrie in 1966.
, (Traducido de https://en.wikipedia.org/wik … (Traducido de https://en.wikipedia.org/wiki/Hidden_semi-Markov_model licencia GFDL y CC-BY-SA 3.0) Un modelo oculto de semi-Markov es un modelo estadístico con la misma estructura que un modelo oculto de Márkov excepto que el proceso inobservable es semi-Márkov en vez de Márkov. Esto significa que la probabilidad de que haya un cambio en el estado oculto depende de la cantidad de tiempo que ha transcurrido desde que entró al estado actual. En contraste a los modelos ocultos de Márkov donde hay una probabilidad constante de cambio de estado dada la supervivencia en el estado hasta ese tiempo.pervivencia en el estado hasta ese tiempo.
, Un modello semi-markoviano nascosto (hidde … Un modello semi-markoviano nascosto (hidden semi-Markov model, HsMM) è un processo stocastico che generalizza i modelli di Markov nascosti consentendo che ogni stato della catena di Markov sottostante al processo possa generare una sequenza di osservazioni, anziché una singola osservazione. La "durata" di uno stato non è più quindi unitaria e può distribuirsi secondo una qualsiasi distribuzione di probabilità continua o discreta. In letteratura si trovano numerose applicazioni di questo modello, per esempio in ambito medico, biologico e finanziario.in ambito medico, biologico e finanziario.
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| rdfs:label |
Modello semi-markoviano nascosto
, Hidden semi-Markov model
, Modelo oculto de semi-Markov
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