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Bayesian inference for continuous-time hidden Markov models with an unknown number of states
We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference for a fixed number of states has not been studied until recen...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550639/ https://www.ncbi.nlm.nih.gov/pubmed/34776654 http://dx.doi.org/10.1007/s11222-021-10032-8 |