An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
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In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.Abstract:
1. Summary. The object of this note is to prove the theorem below and sketch two applications, one to statistical estimation for (proba-bilistic) functions of Markov processes [l] and one to Blakley's model for ecology [4]. 2. Result. THEOREM. Let P(x)=P({xij}) be a polynomial with nonnegative coefficients homogeneous of degree d in its variables {##}. Let x= {##} be any point of the domain D: ## §:(), ]pLi ## = 1, i = l, • • • , p, j=l, • • • , q%. For x= {xij} ££> let 3(#) = 3{##} denote the point of D whose i, j coordinate is (dP\\ \\ f « dP 3(*)<i = (Xij 7—) / 2* *<i — \\ dXij\\(X)// ,-i dXij (»> Then P(3(x))>P(x) unless 3(x)=x. Notation, fi will denote a doubly indexed array of nonnegative integers: fx= {M#}> i = l> • • • > <lu i=l, • • • , A #* then denotes Ilf-iHî-i^* Similarly, c M is an abbreviation for C[ MiJ }. The polynomial P({xij}) is then written P(x) = ]CM V^-In our notation : (1) 3(&)*i = (Z) «Wnys*) / JLH CpiiijX».read more
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Book
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Dan Jurafsky,James Martin +1 more
TL;DR: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
References
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Homogeneous nonnegative symmetric quadratic transformations
TL;DR: Theorem 6 as discussed by the authors states that almost all symmetric homogeneous quadratic transformations give rise to a sequence of iterates which converges pointwise, together with a map of the way stations leading to it.
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