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

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time …

The present dissertation consists of three stand-alone research papers that all deal with factor models from a Bayesian … that can be implemented to conduct Bayesian inference in this kind of model. Chapters 2 and 3 introduce some important …

Bayesian approach: information in longer time series is aggregated into a prior which is then used in the estimation of … from the Georgian economy and find that resulting (Bayesian) VAR models on average perform 7% better than standard …

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 …

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 …