Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of unif...
| Hauptverfasser: | , |
|---|---|
| Format: | Journal article |
| Veröffentlicht: |
2011
|