Computing expectations with continuous p-boxes: Univariate case

Computing expectations with continuous p-boxes: Univariate case

Given an imprecise probabilistic model over a continuous space, computing lower/upper expectations is often computationally hard to achieve, even in simple cases. Because expectations are essential in decision making and risk analysis, tractable methods to compute them are crucial in many applications involving imprecise probabilistic models. We concentrate on p-boxes (a simple and popular mode...

Flexible Univariate Continuous Distributions

Based on a constructive representation, which distinguishes between a skewing mechanism P and an underlying symmetric distribution F , we introduce two flexible classes of distributions. They are generated by nonparametric modelling of either P or F . We examine properties of these distributions and consider how they can help us to identify which aspects of the data are badly captured by simple...

Estimation with Univariate \ Mixed Case

Solving Univariate P-adic Constraints

We describe an algorithm for solving systems of univariate p-adic constraints. In analogy with univariate real constraints, we formalize univariate p-adic constraints as univariate polynomial equations and order comparisons between p-adic values of univariate polynomials. Systems of constraints are arbitrary boolean combinations of such constraints. Our method combines techniques of Presburger ...

A Few Characterizations of the Univariate Continuous Distributions

Various characterizations of distributions, in their generality, are presented in terms of the conditional expectations. Some special  examples are given as well.