A potential implicit particle method for high-dimensional systems

A potential implicit particle method for high-dimensional systems

Blog Article

This paper presents a particle method designed for high-dimensional state estimation.Instead of weighing random forecasts by their distance to given observations, the method samples an ensemble of particles around an optimal solution based on the observations (i.e.

, it is implicit).It Shirt differs from other implicit methods because it includes the state at the previous assimilation time as part of the optimal solution (i.e.

, it is a lag-1 smoother).This is accomplished through the use of a mixture model for the equi-jec 7 background distribution of the previous state.In a high-dimensional, linear, Gaussian example, the mixture-based implicit particle smoother does not collapse.

Furthermore, using only a small number of particles, the implicit approach is able to detect transitions in two nonlinear, multi-dimensional generalizations of a double-well.Adding a step that trains the sampled distribution to the target distribution prevents collapse during the transitions, which are strongly nonlinear events.To produce similar estimates, other approaches require many more particles.