Search Results for author:
Found 2 papers, 1 papers with code
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters
We provide insights on when each sampler should be used and show that they can be combined to form an efficient PG sampler for a model with strong dependencies between states and parameters.
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
