Main content

Efficient Bayesian Inference for Conditionally Autoregressive Models

Show full item record

Title: Efficient Bayesian Inference for Conditionally Autoregressive Models
Author: Angevaare, Justin
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Darlington, GerardaGillis, Daniel
Abstract: We compare the performance of Metropolis-Hastings (MH) and Hamiltonian Monte Carlo (HMC) methods for Bayesian inference, with specific application to conditionally autoregressive (CAR) models. A simulation study is performed which investigates the efficiency of MH and HMC in estimation of the spatial correlation strength parameter of the CAR model. For this, data are simulated at various resolutions and spatial correlation strengths. An application to the relative abundance of Lake Whitefish in Lake Huron is also presented. Many new HMC-based methods have been recently developed, some of which offer significant benefit in performing inference for CAR models.
Date: 2014-04
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.

Files in this item

Files Size Format View Description
Angevaare_Justin_201404_MSc.pdf 1.704Mb PDF View/Open main article

This item appears in the following Collection(s)

Show full item record