Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Bayesian estimation methods form a dynamic branch of statistical inference, utilising Bayes’ theorem to update probabilities in light of new evidence. This framework combines prior knowledge with ...

We suggest a new method for integrating volatility information for estimating the value-at-risk and conditional value-at-risk of a portfolio. This new method is developed from the perspective of ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 46, No. 3 (September/septembre 2018), pp. 399-415 (17 pages) For sparse and high-dimensional data analysis, a valid ...

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 Annals of Applied Statistics, Vol. 8, No. 2 (June 2014), pp. 852-885 (34 pages) Poverty maps are used to aid important political decisions such as allocation of development funds by governments ...

Zhang, J., Cao, J., and Wang, L. (2025) Robust Bayesian Functional Principal Component Analysis. Statistics and Computing. Volume 35, article number 46. pdf Wang, L ...

Decisions on what kind of data to collect to train a machine learning model, and how much, directly impact the accuracy and cost of that system. Bayes error *1 ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

Before the outbreak of coronavirus, the seasonal flu was one of the most dangerous infectious diseases, but a lot of people have trouble telling the difference between a flu and a cold by their ...