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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

Leonard E. Baum, +3 more
- 01 Feb 1970 - 
- Vol. 41, Iss: 1, pp 164-171
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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.

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Patent

Copyright detection and protection system and method

TL;DR: In this article, a method for detecting against unauthorized transmission of digital works comprises the steps of maintaining a registry of information permitting identification of digital copyrighted works, monitoring a network for transmission of at least one packet-based digital signal, extracting at least 1 feature from the at least digital signal and comparing the extracted feature with registry information and applying business rules based on the comparison result.
Journal ArticleDOI

Continuously variable duration hidden Markov models for automatic speech recognition

TL;DR: The solution proposed here is to replace the probability distributions of duration with continuous probability density functions to form a continuously variable duration hidden Markov model (CVDHMM) which is ideally suited to specification of the durational density.
Journal ArticleDOI

A Markov model of heteroskedasticity, risk, and learning in the stock market☆

TL;DR: This paper examined a variety of models in which the variance of a portfolio's excess return depends on a state variable generated by a first-order Markov process and found that the mean excess return moves inversely with the level of risk.
Journal ArticleDOI

NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction

TL;DR: An approach using hidden Markov models (HMMs) to predict novel NLSs in proteins is suggested, and it is found that this method is able to consistently find 37% of the NLS's with a low false positive rate and that the method retains its true positive rate outside of the yeast data set used for the training parameters.
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What is the expectation maximization algorithm

TL;DR: The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models and is good for, and how does it work?
References
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Journal ArticleDOI

An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology

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

The gamma function

Emil Artin, +1 more
Book ChapterDOI

The Gamma Function

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.
Journal ArticleDOI

An Inequality

Joel Brenner