assumption, since point estimators are biased and confidence intervals are difficult to construct. From the Bayesian perspective … treatment effect can be obtained with Bayesian simulation techniques. Our Bayesian inference is applied to an empirical problem …

uncertainty and deviation from normality in a Bayesian framework. The non-normal asset returns are modeled as finite Gaussian … portfolio weights are then constructed so as to maximize agents’ expected utility. Simple experiment suggests that our Bayesian …

imputation, regardless of information loss or noises gain. We study a VAR model with varied frequency data in a Bayesian context … perception of a constrained multivariate normal distribution is crucial to our Gibbs sampler. Furthermore, the Markov property of …

and have a Gaussian mixture distribution conditional on each regime. The model is estimated in a Bayesian framework using …

This paper examines multivariate Tobit system with Scale mixture disturbances. Three estimation methods, namely Maximum … Simulated Likelihood, Expectation Maximization Algorithm and Bayesian MCMC simulators, are proposed and compared via generated … data experiments. The chief finding is that Bayesian approach outperforms others in terms of accuracy, speed and stability …