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On the Foundations of Multinomial Sequence Based Estimation

  • B. John OommenEmail author
  • Sang-Woon Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

This paper deals with the relatively new field of sequence-based estimation which involves utilizing both the information in the observations and in their sequence of appearance. Our intention is to obtain Maximum Likelihood estimates by “extracting” the information contained in the observations when perceived as a sequence rather than as a set. The results of [15] introduced the concepts of Sequence Based Estimation (SBE) for the Binomial distribution. This current paper generalizes these results for the multinomial “two-at-a-time” scenario. We invoke a novel phenomenon called “Occlusion” that can be described as follows: By “concealing” certain observations, we map the estimation problem onto a lower-dimensional binomial space. Once these occluded SBEs have been computed, we demonstrate how the overall Multinomial SBE (MSBE) can be obtained by mapping several lower-dimensional estimates onto the original higher-dimensional space. We formally prove and experimentally demonstrate the convergence of the corresponding estimates.

Keywords

Estimation using sequential information Sequence Based Estimation Estimation of multinomials Fused estimation methods Sequential information 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada
  2. 2.Department of Computer EngineeringMyongji UniversityYonginSouth Korea

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