Bayes linear estimators provide simple Bayesian methods and require a minimum of prior specification. In this article, Bayes linear estimators are derived for a variety of randomized response models.

We indicate the applicability of a Bayesian method of estimating a mean in the face of mean-shift contamination. Simple approximate Bayesian "confidence interval" formulas are obtained by ...

Bayesian predictive density estimation represents a cornerstone of modern statistical inference by integrating prior knowledge with observed data to produce a predictive distribution for future ...

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...

The authors consider a general calibration problem for derivative pricing models, which they reformulate into a Bayesian framework to attain posterior distributions for model parameters. They then ...

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Operations Research & ...