A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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This article is published in Annals of Mathematical Statistics.The article was published on 1970-02-01 and is currently open access. It has received 4618 citations till now. The article focuses on the topics: Examples of Markov chains & Markov chain.read more
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Modeling for text compression
TL;DR: This paper surveys successful strategies for adaptive modeling that are suitable for use in practical text compression systems, and falls into three main classes: finite-context modeling, in which the last few characters are used to condition the probability distribution for the next one.
Book
Theory and Use of the Em Algorithm
Maya R. Gupta,Yihua Chen +1 more
TL;DR: This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM.
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Dynamic model of visual recognition predicts neural response properties in the visual cortex
Rajesh P. N. Rao,Dana H. Ballard +1 more
TL;DR: A hierarchical network model of visual recognition that explains experimental observations regarding neural responses in both free viewing and fixating conditions by using a form of the extended Kalman filter as given by the minimum description length (MDL) principle is described.
Proceedings Article
Hidden Markov Model Induction by Bayesian Model Merging
TL;DR: The algorithm is compared with the Baum-Welch method of estimating fixed-size models, and it is found that it can induce minimal HMMs from data in cases where fixed estimation does not converge or requires redundant parameters to converge.
References
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Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Journal ArticleDOI
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
TL;DR: 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.
Book ChapterDOI
The Gamma Function
Willi Freeden,Martin Gutting +1 more
TL;DR: The Gamma function as discussed by the authors is a generalized factorial function that can be used to estimate the probability distribution of a probability distribution, and it has been used in many applications, e.g., as part of probability distributions.