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104
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NIPS
2007
15 years 2 months ago
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
CLADE
2008
IEEE
15 years 7 months ago
SWARM: a scientific workflow for supporting bayesian approaches to improve metabolic models
With the exponential growth of complete genome sequences, the analysis of these sequences is becoming a powerful approach to build genome-scale metabolic models. These models can ...
Xinghua Shi, Rick Stevens
113
Voted
BMCBI
2004
90views more  BMCBI 2004»
15 years 12 days ago
Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays
Background: Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normal...
Igor Dozmorov, Nicholas Knowlton, Yuhong Tang, Mic...
BROADNETS
2006
IEEE
15 years 6 months ago
Transparent Optimization of Grid Server Selection With Real-Time Passive Network Measurements
Grid services have tremendously simplified the programming challenges in leveraging large-scale distributed comAt the same time, the increased level of abstraction reduces the op...
Marcia Zangrilli, Bruce Lowekamp
100
Voted
DCOSS
2009
Springer
15 years 5 months ago
Compressed RF Tomography for Wireless Sensor Networks: Centralized and Decentralized Approaches
Radio Frequency (RF) tomography refers to the process of inferring information about an environment by capturing and analyzing RF signals transmitted between nodes in a wireless se...
Mohammad A. Kanso, Michael G. Rabbat