Computing highest density regions for continuous univariate distributions with known probability functions

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...

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.

Summarizing CSP Hardness with Continuous Probability Distributions

We present empirical evidence that the distribution of eeort required to solve CSPs randomly generated at the 50% satissable point, when using a backtracking algorithm, can be approximated by two standard families of continuous probability distribution functions. Solvable problems can be modelled by the Weibull distribution , and unsolvable problems by the lognormal distribution. These distribu...

Extracting Object Regions Using Locally Estimated Probability Density Functions

In this paper, a novel method for estimating a precise object region using a given rough object region is proposed. For determining whether each pixel belongs to an object or not, the proposed method estimates a joint probability density function (joint p.d.f.) of position, color, and class (object or background). For each pixel, the class with a higher joint p.d.f. is selected. The joint p.d.f...

On Phase II Monitoring of the Probability Distributions of Univariate Continuous Processes