Using the Bayesian posterior distribution of market parameters we define selfadjusting uncertainty regions for the robust mean-variance problem. Under a normal-inverse-Wishart conjugate assumption for ...

The aim of operational risk modeling is to provide a reasonably accurate, reasonably precise and reasonably robust estimation of capital requirements, including a level of sensitivity that is ...

We consider the Bayes estimator δ0 for a Gaussian signal process observed in the presence of additive Gaussian noise under contamination of the signal or noise by QN-laws, introduced by Gualtierotti ...

Bayesian approaches have emerged as powerful tools in forensic anthropology, particularly in the domain of age estimation. By integrating prior knowledge with new observational data, these methods ...

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 ...

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 ...