Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.238 SNIP: 0.464 CiteScore™: 0.27

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

Выпуски:
Том 51, 2019 Том 50, 2018 Том 49, 2017 Том 48, 2016 Том 47, 2015 Том 46, 2014 Том 45, 2013 Том 44, 2012 Том 43, 2011 Том 42, 2010 Том 41, 2009 Том 40, 2008 Том 39, 2007 Том 38, 2006 Том 37, 2005 Том 36, 2004 Том 35, 2003 Том 34, 2002 Том 33, 2001 Том 32, 2000 Том 31, 1999 Том 30, 1998 Том 29, 1997 Том 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v31.i1-3.460
pages 60-74

Identification of Statistical Parameters in one Model of Conditional Independence

M. I. Shlezinger
International Scientific and Training Center of Information Technologies and Systems, National Academy of Sciences and Ministry of Education of Ukraine, Kiev

Краткое описание

A model of conditional independence is studied as well as the problem of its identifiability when a definite subset of its parameter is hidden and not observable. The identification problem for such type of models is formulated as a special case of the well-known problem of non-supervised learning or self-learning. Commonly used procedures of self-learning are based on the maximum likelihood estimation of statistical parameters of the model. These procedures perform a certain type of hill-climbing and converge to the local rather than global maximum of the likelihood function. The main new result of the investigation is that for the model under consideration there exists an algorithm of self-learning, that finds a global maximum of likelihood function though the likelihood function is not unimodal.


Articles with similar content:

Sufficient Conditions of Stabilization and Estimation of Systems with Delays on the Basis of Incomplete Observations
Journal of Automation and Information Sciences, Vol.28, 1996, issue 3-4
A. V. Danilin
The Use of Fuzzy A Priori Information for Estimation of Regression Parameters
Journal of Automation and Information Sciences, Vol.35, 2003, issue 1
Arnold S. Korkhin, Victor N. Mizernyi
AN EFFICIENT NUMERICAL METHOD FOR UNCERTAINTY QUANTIFICATION IN CARDIOLOGY MODELS
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 3
Zhiwen Zhang, Xindan Gao, Wenjun Ying
Nonstochastic Approach to Determining the Dimension and Parameters of Linear Autoregressive Models by the Input and Output Variables Measurements
Journal of Automation and Information Sciences, Vol.42, 2010, issue 1
Nikolay N. Salnikov, Igor A. Kremenetskiy
Construction of Stability Domain of Digital Linear Systems in Space of Parameters Using Method of Discrete D-Partition
Journal of Automation and Information Sciences, Vol.49, 2017, issue 2
Sergey L. Movchan, Leonid T. Movchan