Alternatives to Lavine's algorithm for calculation of posterior bounds given convex sets of distributions

Fabio Cozman
tech. report CMU-RI-TR-97-38, Robotics Institute, Carnegie Mellon University, December, 1997


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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",
}