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» Optimized mixed Markov models for motif identification
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ICARCV
2008
IEEE
170views Robotics» more  ICARCV 2008»
13 years 11 months ago
Mixed state estimation for a linear Gaussian Markov model
— We consider a discrete-time dynamical system with Boolean and continuous states, with the continuous state propagating linearly in the continuous and Boolean state variables, a...
Argyris Zymnis, Stephen P. Boyd, Dimitry M. Gorine...
CDC
2009
IEEE
130views Control Systems» more  CDC 2009»
13 years 9 months ago
Mixed linear system estimation and identification
We consider a mixed linear system model, with both continuous and discrete inputs and outputs, described by a coefficient matrix and a set of noise variances. When the discrete inp...
Argyrios Zymnis, Stephen P. Boyd, Dimitry M. Gorin...
CSDA
2010
208views more  CSDA 2010»
13 years 5 months ago
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses t...
Raffaele Argiento, Alessandra Guglielmi, Antonio P...
BMCBI
2010
123views more  BMCBI 2010»
13 years 11 days ago
Predicting conserved protein motifs with Sub-HMMs
Background: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is th...
Kevin Horan, Christian R. Shelton, Thomas Girke
ICASSP
2011
IEEE
12 years 9 months ago
Collaborative sources identification in mixed signals via hierarchical sparse modeling
A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on CHiLasso, a convex collaborative hierarchical s...
Pablo Sprechmann, Ignacio Ramírez, Pablo Ca...