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» On Bayesian model and variable selection using MCMC
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ICML
2004
IEEE
15 years 10 months ago
Parameter space exploration with Gaussian process trees
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are ...
Robert B. Gramacy, Herbert K. H. Lee, William G. M...
CP
2003
Springer
15 years 2 months ago
Terminating Decision Algorithms Optimally
Incomplete decision algorithms can often solve larger problem instances than complete ones. The drawback is that one does not know whether the algorithm will finish soon, later, ...
Tuomas Sandholm
IJCNN
2007
IEEE
15 years 4 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
98
Voted
METMBS
2004
196views Mathematics» more  METMBS 2004»
14 years 11 months ago
An Open Problem in RNA Secondary Structure Prediction by the Comparative Approach
Abstract One approach to predict the secondary structure of RNA is the comparative approach. This approach is used when alignment of several homologous sequences of a RNA is availa...
Stefan Engelen, Fariza Tahi
BMCBI
2010
115views more  BMCBI 2010»
14 years 9 months ago
Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat l
Background: Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However...
Tung T. Nguyen, Richard R. Almon, Debra C. DuBois,...