Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/141821
Title: MULTILEVEL PARTICLE FILTERS FOR CONTINUOUS TIME PROCESSES
Authors: OSEI PRINCE PEPRAH
ORCID iD:   orcid.org/0000-0003-2240-4324
Keywords: Multilevel particle filters, sequential Monte Carlo methods, normalizing constants, option pricing, Levy processes, diffusion processes
Issue Date: 25-Jan-2018
Citation: OSEI PRINCE PEPRAH (2018-01-25). MULTILEVEL PARTICLE FILTERS FOR CONTINUOUS TIME PROCESSES. ScholarBank@NUS Repository.
Abstract: In recent times, practical problems of interest such as continuous time processes and its applications have attracted a lot of scientific research. Typical computational issues arising from these problems is the computation of expectations of functional of these processes. This has called for developing fast computational methods to achieve accurate estimates at a minimal cost in solving these problems. Efficient algorithms are then needed to handle the computational burden associated with solving these continuum problems. This thesis addresses these issues by developing and applying one of these algorithms, called the multilevel particle filter, for inference problems arising in solving these continuum problems such as stochastic differential equation. The continuous time processes considered are diffusion processes and general Levy processes. The computational savings obtained in applying the multilevel particle filter compared with the standard particle filter is illustrated with several practical application problems.
URI: http://scholarbank.nus.edu.sg/handle/10635/141821
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
OseiPP.pdf1.08 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

66
checked on Jul 10, 2020

Download(s)

9
checked on Jul 10, 2020

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.