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ANSS
2006
IEEE
14 years 1 months ago
A New Approach for Computing Conditional Probabilities of General Stochastic Processes
In this paper Hidden Markov Model algorithms are considered as a method for computing conditional properties of continuous-time stochastic simulation models. The goal is to develo...
Fabian Wickborn, Claudia Isensee, Thomas Simon, Sa...
BMCBI
2006
143views more  BMCBI 2006»
13 years 7 months ago
Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction
Background: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because ...
Eric D. Scheeff, Philip E. Bourne
BMCBI
2010
105views more  BMCBI 2010»
13 years 7 months ago
Subdivision of the MDR superfamily of medium-chain dehydrogenases/reductases through iterative hidden Markov model refinement
Background: The Medium-chain Dehydrogenases/Reductases (MDR) form a protein superfamily whose size and complexity defeats traditional means of subclassification; it currently has ...
Joel Hedlund, Hans Jörnvall, Bengt Persson
DICTA
2003
13 years 9 months ago
Gesture Classification Using Hidden Markov Models and Viterbi Path Counting
Human-Machine interfaces play a role of growing importance as computer technology continues to evolve. Motivated by the desire to provide users with an intuitive gesture input syst...
Nianjun Liu, Brian C. Lovell
CVIU
2006
162views more  CVIU 2006»
13 years 7 months ago
Unsupervised scene analysis: A hidden Markov model approach
This paper presents a new approach to scene analysis, which aims at extracting structured information from a video sequence using directly low-level data. The method models the se...
Manuele Bicego, Marco Cristani, Vittorio Murino