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 ...

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

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 ...