Name
Abstract | ||
|---|---|---|
Hidden semi-Markov models are a generalization of the well-known hidden Markov model. They allow for a greater flexibility of sojourn time distributions, which implicitly follow a geometric distribution in the case of a hidden Markov chain. The aim of ... |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.csda.2008.10.025 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
sojourn time distribution,geometric distribution,bayesian computing,hidden semi-markov model,evolutionary monte carlo,hidden markov chain,greater flexibility,well-known hidden markov model,markov chain monte carlo,markov chain,genetic operator,stationary distribution,bayesian analysis | Monte Carlo method in statistical physics,Econometrics,Rejection sampling,Monte Carlo method,Monte Carlo algorithm,Markov chain Monte Carlo,Particle filter,Algorithm,Hybrid Monte Carlo,Statistics,Mathematics,Gibbs sampling | Journal |
Volume | Issue | ISSN |
54 | 3 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
3 | 0.41 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Hu | 1 | 8 | 1.17 |
| Kam-Wah Tsui | 2 | 12 | 3.37 |