Bayesian Integration for Assessing the Quality of the Laplace Approximation

Author: 
Date created: 
2017-11-24
Identifier: 
etd10444
Keywords: 
Bayesian methodology, Partial Differential Equation, Gaussian Process in Machine learning
Abstract: 

Nuisance parameters increase in number with additional data collected. In dynamic models, this typically results in more parameters than observations making direct estimation intractable. The Laplace Approximation is the standard tool for approximating the high dimensional integral required to marginalize over the nuisance parameters. However the Laplace Approximation relies on asymptotic arguments that are unobtainable for nuisance parameters. The way to assess the quality of the Laplace Approximation relies on much slower MCMC based methods. In this work, a probabilistic integration approach is used to develop a diagnostic for the quality of the Laplace Approximation.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
David Alexander Campbell
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: