Dissertation/Thesis Abstract

Tenability and Computability of Generalized Pólya Urns
by Konzem, Scott R., Ph.D., The George Washington University, 2017, 103; 10263413
Abstract (Summary)

Urn models have a storied part in the history of probability and have been studied extensively over the past century for their wide range of applications. We analyze a generalized class of urn models introduced in the past decade, the so-called "multiset" class, in which more than one ball is sampled at a time. We investigate sufficient conditions for a multiset urn process to be tenable, meaning the process can continue indefinitely without getting stuck. We fully characterize the "strongly tenable" class of Pólya urn schemes, which is tenable under any starting conditions that allow the process to begin. We find several "weakly tenable" classes of Pólya urn schemes that are tenable only under restricted starting conditions. We enumerate the size of some of these tenable classes using combinatorics, probabilistically analyze them, and provide an algorithm to assess the tenability of an arbitrary urn scheme using breadth-first search. We further analyze the computational complexity of the tenability problem itself. By showing how to encode the Boolean satisfiability problem within a Pólya urn scheme, we find that the problem of determining whether a multiset urn scheme is untenable is in the complexity class NP-hard, and this places constraints on the kinds of tenability theorems we can hope to find. Finally, we analyze a generalized “fault tolerant” urn model that can take action to avoid getting stuck, and by showing that this model is Turing-equivalent, we show that the tenability problem for this model is undecidable.

Indexing (document details)
Advisor: Mahmoud, Hosam M.
Commitee: Balaji, Srinivasan, Barta, Winfried
School: The George Washington University
Department: Statistics
School Location: United States -- District of Columbia
Source: DAI-B 78/08(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Statistics, Computer science
Keywords: Pólya urn, Random structure
Publication Number: 10263413
ISBN: 9781369684834
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