On auxiliary variables and many-core architectures in computational statistics

Thesis

Emerging many-core computer architectures provide an incentive for computational methods to exhibit specific types of parallelism. Our ability to perform inference in Bayesian statistics is often dependent upon our ability to approximate expectations of functions of random variables, for which Monte Carlo methodology provides a general purpose solution using a computer. This thesis is primarily concerned with exploring the gains that can be obtained by using many-core architectures to acce...

Expand abstract

Authors: Lee, A

• Publication date: 2011
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Markov chain Monte Carlo; Bayesian Inference; Sequential Monte Carlo
• Subjects: Computationally-intensive statistics; Statistics (see also social sciences)