Scalable inference in state-space models

Thesis

This thesis provides a set of novel Monte Carlo methods to perform Bayesian inference, with an emphasis on a state-space modelling framework. The thesis comprises three self-contained research papers. The first paper works at the interface of modelling and inference, extending existing work to develop a new model for medium and high-dimensional data possessing step-changes changes in the correlation structure. A bespoke inference routine is developed, with experimental results demonstrating t...

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Authors: Middleton, L

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: state space model; coupling; Markov chain Monte Carlo; unbiased estimation; Monte Carlo; parallel computation; particle filter
• Subjects: Computational statistics