Data-driven Particle Filters for Particle Markov Chain Monte Carlo
thesisposted on 19.06.2017 by KIN PONG LEUNG
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
This thesis proposes two computationally efficient algorithms suitable for analysing complex time series models under the Bayesian statistical paradigm. Certain critical theoretical properties of the algorithms are established, with their superior empirical performance demonstrated via a suite of controlled simulation experiments. A comprehensive review of the literature is provided, and the performance of many existing methods compared with that of the proposed algorithms. The thesis explores three particular classes of time series models, however the approach is general and finds application across the economic and other social science disciplines, as well as disciplines from the physical sciences.