Material Detail

Shadow Dirichlet for Restricted Probability Modeling

This video was recorded at Video Journal of Machine Learning Abstracts - Volume 1. Although the Dirichlet distribution is widely used, the independence structure of its components limits its accuracy as a model. The proposed shadow Dirichlet distribution manipulates the support in order to model probability mass functions (pmfs) with dependencies or constraints that often arise in real world problems, such as regularized pmfs, monotonic pmfs, and pmfs with bounded variation. We describe some properties of this new class of distributions, provide maximum entropy constructions, give an expectation-maximization method for estimating the mean parameter, and illustrate with real data.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

Go to Material

Bookmark / Add to Course ePortfolio

Create a Learning Exercise

Add Accessibility Information

Rate

Add a Comment

Quality

User Rating
Comments
Learning Exercises
Bookmark Collections
Course ePortfolios
Accessibility Info

Report Broken Link
Report as Inappropriate

More about this material

Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: Maya Gupta, Department of Electrical Engineering, University of Washington
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States