The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional …

modelling to integrated Bayesian modelling using MCMC, via bootstrapping and stochastic simulation. It also covers a broad range … harvest rate. Our approach uses individual-based modeling and Bayesian analysis to investigate the effect of shadows on … statistical model, and estimates parameters using both maximum likelihood and Bayesian MCMC.The sixth chapter (Hoyle 2002, peer …

In this thesis we address problems associated with financial modelling from a Bayesian point of view. Specifically, we … Bayesian model average price. In tests on local volatility models it is shown that this price is closer than the prices of … measures from the latter class are provided using the Bayesian posterior. These are used to value the model uncertainty for a …

borrowed from Bayesian estimation literature by extending MCMC algorithm with "Rao-Blackwellization" of the posterior density …. Compared to two alternative estimation methods in context of Bayesian estimation of DSGE models, this augmentation method …

Bayesian methodology it will be possible to create a comprehensive predictive model of the health burdens being faced by … contributions of this thesis is coherent incorporation of prior information into the proposed expert model. The Bayesian models were …

ofthe method which, although less powerful, is not affected by convergence issues. In Chapter 6, I describe Bayesian …