Parameter estimation for state space models using sequential Monte Carlo algorithms
Christopher Nemeth · Lancaster EPrints (Lancaster University) · 2014
State space models represent a flexible class of Bayesian time series models which can be applied to model latent state stochastic processes.Sequential Monte Carlo (SMC) algorithms, also known as particle filters, are perhaps the most widely used methodology for inference in such models, particularly when the model is nonlinear and cannot be evaluated analytically.The SMC methodology allows for the sequential Declaration