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Found 3 papers, 1 papers with code
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.
Continuous Time Particle Filtering for fMRI
We construct a biologically motivated stochastic differential model of the neural and hemodynamic activity underlying the observed Blood Oxygen Level Dependent (BOLD) signal in Functional Magnetic Resonance Imaging (fMRI).
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