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Title: Parameter estimation for state space models using sequential Monte Carlo algorithms
Author: Nemeth, Christopher
ISNI:       0000 0004 5355 9721
Awarding Body: Lancaster University
Current Institution: Lancaster University
Date of Award: 2014
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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 analysis of state space models in online settings for fast inference, but can also be applied to study online problems. This area of research has grown rapidly over the past 20 years and has lead to the development of important theoretical results.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available