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 …

A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on …

data from the LNG industry are very sparse. Bayesian estimation isidentified as one method to compensate for its weaknesses …. It can update the generic datawith plant specific data.Based on Bayesian estimation, the frequencies of initiating events …-PFDconversion method was used instead. By the combination of Bayesian estimation andLOPA procedures, the Bayesian-LOPA methodology was …

strategy is to improve the Minnesota prior, which is frequently used for Bayesian VAR models. The improvement is achieved in …

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 …