This document provides an in-depth look at the process used in trying to solve real issues with the User Experience of a social bookmarking application. While it might be easy to simply take the first solution that works and assume that it’s the best solution, the first solution is very rarely the best solution. We found several solutions to several problems, and many of them worked and appeared to be decent solutions. It was only upon further investigation and doing more detailed research that we found hidden flaws in some solutions, issues with user satisfaction in other solutions, and even found some solutions that broke entirely under certain conditions.
This paper will describe the problems we faced in detail and then provide an explanation of the solutions evaluated for each problem, including the benefits and drawbacks of each solution. We will also identify the final solution chosen and why it was chosen.
The Distributome Project is an open-source, open content-development project for exploring, discovering, learning and computational utilization of diverse probability distributions. Probability distributions are a special class of functions defined in terms of integrals of positive density functions. Distribution functions are very useful in representation, modeling and interpretation of various observable processes and natural phenomena. Probability densities are a non-negative functions that integrate to one over the real numbers. A probability density function can be seen as a smooth version of a frequency histogram.
The interactive Distributome graphical user interface provides the following core functions:
* visually traverse the space of all well-defined (named) distributions;
* explore the relations between different distributions;
* distribution search by keyword, property and type;
* obtain qualitative (e.g., analytic form of density function) and quantitative (e.g., critical and probability values) information about each distribution;
* discover references and additional distribution resources.
This project was initiated in 2008 by the UAH Virtual Laboratories in Probability and Statistics, the UCLA Statistics Online Computational Resource and the OSU Mathematical Biosciences Institute
E. Ollila. (2017)cite arxiv:1706.10066Comment: Accepted in the 25th European Signal Processing Conference (EUSIPCO 2017), published by EURASIP, scheduled for Aug. 28 - Sep. 2 in Kos island, Greece.
G. Letac, and H. Massam. Journal of Multivariate Analysis, 99 (7):
1393 - 1417(2008)Special Issue: Multivariate Distributions, Inference and Applications in Memory of Norman L. Johnson.