Markov model

Known as: Hierarchical Markov model, Markov models 
In probability theory, a Markov model is a stochastic model used to model randomly changing systems where it is assumed that future states depend… (More)
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Topic mentions per year

1966-2017
050019662017

Papers overview

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Highly Cited
2001
Highly Cited
2001
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the… (More)
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Highly Cited
2001
Highly Cited
2001
I n t r o d u c t i o n Computational analysis is increasingly important for inferring the functions and structures of proteins… (More)
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Highly Cited
1999
Highly Cited
1999
In this paper we form a finite-state Markov channel model to represent Rayleigh fading channels. We develop and analyze a… (More)
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Highly Cited
1998
Highly Cited
1998
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we… (More)
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Highly Cited
1998
Highly Cited
1998
A novel method to model and predict the location and orientation of alpha helices in membrane-spanning proteins is presented. It… (More)
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Highly Cited
1998
Highly Cited
1998
MOTIVATION A new hidden Markov model method (SAM-T98) for finding remote homologs of protein sequences is described and evaluated… (More)
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Highly Cited
1996
Highly Cited
1996
Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting… (More)
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Highly Cited
1995
Highly Cited
1995
Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series… (More)
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Highly Cited
1992
Highly Cited
1992
This paper proposes a new human action recognition method baaed on a Hidden Markov Model (HMM). We do not adopt model-based top… (More)
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Highly Cited
1989
Highly Cited
1989
In this paper, we extend hidden Markov modeling to speaker-independent phone recognition. Using multiple codebooks of various LPC… (More)
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