The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

This paper presents alternatives to Lavine's algorithm, currently the most popular method for calculation of expectation bounds induced by sets of probability distributions. The White-Snow algorithm is first analyzed and demonstrated to be superior to Lavine's algorithm in a variety of situations. The calculation of posterior bounds is then reduced to a fractional programming problem. From the unifying perspective of fractional programming, Lavine's algorithm is identical to Dinkelbach's algorithm, and the White-Snow algorithm is essentially identical to the Charnes-Cooper transformation. A novel algorithm for expectation bounds is given for the situation where both prior and likelihood functions are specified as convex sets of distributions.

Notes

Sponsor: NASA, CNPq

Grant ID: NAGW-1175

Number of pages: 18

Text Reference

Fabio Cozman, "Alternatives to Lavine's algorithm for calculation of posterior bounds given convex sets of distributions," tech. report CMU-RI-TR-97-38, Robotics Institute, Carnegie Mellon University, December, 1997

BibTeX Reference

@techreport{Cozman_1997_456,
   author = "Fabio Cozman",
   title = "Alternatives to Lavine's algorithm for calculation of posterior bounds given convex sets of distributions",
   booktitle = "",
   institution = "Robotics Institute",
   month = "December",
   year = "1997",
   number= "CMU-RI-TR-97-38",
   address= "Pittsburgh, PA",
}

The Robotics Institute

Menu