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 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 ...

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 ...

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 ...

The factor model is an important construct for both portfolio managers and researchers in modern finance. For practitioners, factor model coefficients are used to guide the construction of optimal ...

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 ...

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 ...

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 ...

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 ...