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 ...

The paper constructs a new output gap measure for Vietnam by applying Bayesian methods to a two-equation AS-AD model, while treating the output gap as an unobservable series to be estimated together ...

New relations are derived for Bayesian estimation of parameters in nonlinear multiresponse models. Modal and interval estimates are provided for the parameter vector θ of a multiresponse expectation ...

The empirical Bayes estimation is based on Bayes statistics. It integrates a correlation method with statistical estimations to integrate prior knowledge or beliefs about the parameters of the dataset ...

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 ...

We propose a new approach for estimating operational risk models under the loss distribution approach from historically observed losses. Our method is based on extreme value theory and, being Bayesian ...

Cobimetinib Plus Vemurafenib in Patients With Colorectal Cancer With BRAF Mutations: Results From the Targeted Agent and Profiling Utilization Registry (TAPUR) Study We divided the borrowing ...

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 ...