Loss Given Default (LGD) is the loss borne by the bank when a customer defaults on a loan. LGD for unsecured retail loans is often found difficult to model. In the frequentist (non-Bayesian) two-step approach, two separate regression models are estimated independently, which can be considered potentially problematic when trying to combine them to make predictions about LGD. The result is a poin...

Loss Given Default (LGD) is the loss borne by the bank when a customer defaults on a loan. LGD for unsecured retail loans is often found difficult to model. In the frequentist (nonBayesian) two-step approach, two separate regression models are estimated independently, which can be considered potentially problematic when trying to combine them to make predictions about LGD. The result is a point...

There isn’t a single authorative definition of a hierarchical model. Gelman et al. (1995) discuss two definitions: 1. “Estimating the population distribution of unonobserved parameters” 2. “Multiple parameters related by the structure of the problem” Intuitively, knowing something about one “experiment” tells us something about another. For example: Multiple similar experiments Similar measurem...

Recent studies have proposed hierarchical Bayesian model of visual, or other multidimensional signal processing the scaling factors of linear components in which are represented by higherorder components. Here we constructed a two-layer Bayesian model of the early visual cortex and investigated the model properties to provide a explanation of how visual system acquires the even-symmetric respon...

With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects mo...