In this paper, we propose a unified Bayesian approach for multivariate structured additive distributional regression analysis where inference is applicable to a huge class of multivariate response distributions, comprising continuous, discrete and latent models, and where each parameter of these...

In this paper, we propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of...

Proper scoring rules are scoring methods that incentivize honest reporting of subjective probabilities, where an agent strictly maximizes his expected score by reporting his true belief. The implicit assumption behind proper scoring rules is that agents are risk neutral. Such an assumption is...

Calibration, the statistical consistency of forecast distributions and observations, is a central requirement for probabilistic predictions. Calibration of continuous forecasts has been widely discussed, and significance tests are commonly used to detect whether a prediction model is...

Many recommendation and decision processes depend on eliciting evaluations of opportunities, products, and vendors. A scoring system is devised that induces honest reporting of feedback. Each rater merely reports a signal, and the system applies proper scoring rules to the implied posterior...

A scoring rule is a reward function for eliciting or evaluating forecasts expressed as discrete or continuous probability distributions. A rule is strictly proper if it encourages the forecaster to state his true subjective probabilities, and effective if it is associated with a metric on the...