Title
Parameter Estimation with Sequential Monte Carlo
Speaker
Lixin Lang, The Ohio State University
Abstract
Sequential Monte Carlo (SMC), also known as particle filtering, is a powerful simulation method to perform Bayesian inference for state space models. It estimates system states by recursively updating samples and weights, or the so called particles, to approximate the underlying posterior distribution of system state, which evolves over time as new observations arrive sequentially.
However, with a finite particle set, as constrained by available computing resources, the practical performance of SMC could be sensitive to the specified prior. Other restrictions with applying SMC include handling constraints and parameter estimation. Regular SMC cannot handle constraints that are common in practical systems. Parameter estimation in dynamic models has been a challenging problem since system state estimation itself is already a tough one.
In our research, we introduced a numerical smoothing method, moving horizon estimation, to get smoothed prior information for the start of SMC simulation. For parameter estimation, we developed a moving window EM algorithm to get the MLE of model parameters and simultaneous state estimation.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
