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Further, algorithms based on stochastic variants of EM have so far not implemented features commonly found in other motif discovery algorithms, including the ability to automatically determine the most likely motif width from a range of plausible values.
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F einberg and I rizarry (2010) recently proposed a heritable and selectable mechanism for this based on stochastic epigenetic variation.
The characteristics of our baseline epidemics were consistent with other simulations based on stochastic individual-based models, specifically [7] [11].
This algorithm utilises a probabilistic model based on stochastic context free grammar and secondary structure-based filtering systems (SCFG).
NORMA is based on stochastic gradient descent.
Reliability analysis is based on stochastic discrete event models like stochastic Petri nets.
Our mechanism is based on stochastic datagram relocation.
The algorithm is developed based on stochastic finite-element method.
KF technique makes predictions based on stochastic and dynamic structures.
The solution method is based on stochastic successive linear programming.
Computational models, routinely based on stochastic optimization, support these decisions.
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