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» Bayesian multiscale analysis for time series data
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ICML
2006
IEEE
14 years 8 months ago
Hidden process models
We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling h...
Rebecca Hutchinson, Tom M. Mitchell, Indrayana Rus...
BMCBI
2010
146views more  BMCBI 2010»
13 years 7 months ago
Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers
Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding pro...
Ross K. Shepherd, Theo H. E. Meuwissen, John A. Wo...
BMCBI
2008
86views more  BMCBI 2008»
13 years 7 months ago
Piecewise multivariate modelling of sequential metabolic profiling data
Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the...
Mattias Rantalainen, Olivier Cloarec, Timothy M. D...
WABI
2005
Springer
124views Bioinformatics» more  WABI 2005»
14 years 1 months ago
Reconstructing Metabolic Networks Using Interval Analysis
Recently, there has been growing interest in the modelling and simulation of biological systems. Such systems are often modelled in terms of coupled ordinary differential equation...
Warwick Tucker, Vincent Moulton
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 9 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider