What is it about?
A new class of continuous probability distributions that are flexible enough to represent a wide range of uncertainties such as those that commonly arise in business, technology, and science. These distributions are highly flexible, easy to parameterize with data, and fast and easy to simulate.
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Why is it important?
In a world with rapidly increasing data, more flexible and easier-to-use probability distributions are needed to represent that data.
Perspectives
250 years ago, Bayes provided the rationale and foundation for continuous probability distributions that can have virtually any shape. Quantile-parameterized distributions provide a technology that can these ideas practical.
Tom Keelin
Read the Original
This page is a summary of: Quantile-Parameterized Distributions, Decision Analysis, September 2011, INFORMS,
DOI: 10.1287/deca.1110.0213.
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