Download or read book Efficient Bayesian Inference in Non-linear Switching State Space Models Using Particle Gibbs Sampling Approaches written by Jaeho Kim and published by . This book was released on 2016 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops a new Bayesian algorithm to efficiently estimate non-linear/non-Gaussian state space models with abruptly changing parameters. Within the Particle Gibbs framework developed by Andrieu et al. (2010), the proposed algorithm effectively combines two ideas: ancestor sampling and a partially deterministic sequential Monte Carlo method. In the proposed approach, the discrete latent state variable that governs the switching behavior of a complex dynamic system is deterministically generated to fully diversify particles, and ancestor sampling enables complete exploitation of the various generated particles. Without a large number of particles and sophisticated tailored importance distributions, the newly developed PG sampler is shown to be both easy to implement and computationally efficient, and it substantially outperforms a standard PG technique.