A study is made of the simple empirical Bayes estimators proposed by Robbins (1956). These estimators are compared with `best' conventional estimators in terms of ...

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 ...

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 ...

Bayesian inference has emerged as a powerful tool in the analysis of queueing systems, blending probability theory with statistical estimation to update beliefs about system parameters as new data ...

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social ...

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 & ...