A review on computation methods for Bayesian state-space model with case studies

Repository

A review on computation methods for Bayesian state-space model with case studies

Show full record

Title: A review on computation methods for Bayesian state-space model with case studies
Author: Yang, Mengta, 1979-
Abstract: Sequential Monte Carlo (SMC) and Forward Filtering Backward Sampling (FFBS) are the two most often seen algorithms for Bayesian state space models analysis. Various results regarding the applicability has been either claimed or shown. It is said that SMC would excel under nonlinear, non-Gaussian situations, and less computationally expansive. On the other hand, it has been shown that with techniques such as Grid approximation (Hore et al. 2010), FFBS based methods would do no worse, though still can be computationally expansive, but provide more exact information. The purpose of this report to compare the two methods with simulated data sets, and further explore whether there exist some clear criteria that may be used to determine a priori which methods would suit the study better.
Department: Mathematics
Subject: Bayesian State space models Sequential Monte Carlo Markov Chain Monte Carlo Forward filtering backward sampling
URI: http://hdl.handle.net/2152/ETD-UT-2010-05-1302
Date: 2010-05

Files in this work

Download File: YANG-MASTERS-REPORT.pdf
Size: 996.5Kb
Format: application/pdf

This work appears in the following Collection(s)

Show full record


Advanced Search

Browse

My Account

Statistics

Information