Year: 2009
Type: dissertation
Abstract: ... models to general dynamic models. The method is then applied to a financial stochastic volatility model on US $- British £ exchange rates and identifies a limitation in the current state-of-the-art method in that field. Bayesian inference in the previous chapter is accomplished through Markov chain Monte Carlo which is suitable for batch analyses, but computationally limiting in sequential analysis. Chapter 6 introduces sequential Monte Carlo. It discusses two methods currently available for simultaneous sequential estimation of latent states and fixed parameters and then introduces a novel algorithm that reduces the key, limiting degeneracy issue while being usable in a wide model class. This new method is applied to a biological model discussed in Chapter 4. Chapter 7 implements and compares novel sequential Monte Carlo algorithms in the disease surveillance context modeling influenza epidemics. Finally, Chapter 8 suggests areas for future work in both modeling and Bayesian inference. Several appendices provide detailed technical support material as well as relevant related work.
Source: DukeSpace (Duke University)
Author: Jarad Niemi
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2120878650