Sequential Monte Carlo Methods for System Identification
arXiv:1503.06058 · doi:10.1016/j.ifacol.2015.12.224
Abstract
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.
In proceedings of the 17th IFAC Symposium on System Identification (SYSID). Added cover page
References in corpus (6)
- The pseudo-marginal approach for efficient Monte Carlo computations
- Sharp failure rates for the bootstrap particle filter in high dimensions
- Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
- Sequential Monte Carlo smoothing for general state space hidden Markov models
- A Stable Particle Filter in High-Dimensions
- Nested Sequential Monte Carlo Methods