DocumentCode :
3511193
Title :
Algebraic computation of pattern maximum likelihood
Author :
Acharya, Jayadev ; Das, Hirakendu ; Orlitsky, Alon ; Pan, Shengjun
Author_Institution :
UC San Diego, San Diego, CA, USA
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
400
Lastpage :
404
Abstract :
Pattern maximum likelihood (PML) is a technique for estimating the probability multiset of an unknown distribution. With any random sample, it associates the distribution maximizing the probability of its pattern. The required computation is a maximization of a monomial symmetric polynomial over the monotone simplex. The PML of only very few patterns have been found analytically, and for other patterns, the PML has been approximated by a heuristic algorithm. Taking an algebraic approach, we determine the PML of short patterns by solving a system of multivariate polynomial equations using the method of resultants. Using this approach, we determine the PML of the pattern 1112234, the last length-7 pattern whose PML was unknown. Under two plausible but yet unproved assumptions on the optimal alphabet size and the number of distinct probabilities, we also find the PML distribution of all previously unknown patterns of length up to 14.
Keywords :
maximum likelihood estimation; polynomials; statistical distributions; PML distribution; algebraic computation; heuristic algorithm; length-7 pattern; monomial symmetric polynomial maximization; monotone simplex; multivariate polynomial equations; pattern maximum likelihood; probability multiset estimation; Information theory; Lead; Mathematical model; Maximum likelihood estimation; Polynomials; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
Conference_Location :
St. Petersburg
ISSN :
2157-8095
Print_ISBN :
978-1-4577-0596-0
Electronic_ISBN :
2157-8095
Type :
conf
DOI :
10.1109/ISIT.2011.6034155
Filename :
6034155
Link To Document :
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