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» A Guided Monte Carlo Approach to Optimization Problems
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IJCNN
2000
IEEE
15 years 2 months ago
On MCMC Sampling in Bayesian MLP Neural Networks
Bayesian MLP neural networks are a flexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distri...
Aki Vehtari, Simo Särkkä, Jouko Lampinen
MICCAI
2002
Springer
15 years 10 months ago
Regularized Stochastic White Matter Tractography Using Diffusion Tensor MRI
The development of Diffusion Tensor MRI has raised hopes in the neuro-science community for in vivo methods to track fiber paths in the white matter. A number of approaches have be...
Mats Björnemo, Anders Brun, Ron Kikinis, Carl...
ECCV
2006
Springer
15 years 1 months ago
Human Pose Tracking Using Multi-level Structured Models
Tracking body poses of multiple persons in monocular video is a challenging problem due to the high dimensionality of the state space and issues such as inter-occlusion of the pers...
Mun Wai Lee, Ramakant Nevatia
WSC
2004
14 years 11 months ago
New Event-driven Sampling Techniques for Network Reliability Estimation
Exactly computing network reliability measures is an NPhard problem. Therefore, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. ...
Abdullah Konak, Alice E. Smith, Sadan Kulturel-Kon...
FMOODS
2007
14 years 11 months ago
A Probabilistic Formal Analysis Approach to Cross Layer Optimization in Distributed Embedded Systems
We present a novel approach, based on probabilistic formal methods, to developing cross-layer resource optimization policies for resource limited distributed systems. One objective...
Minyoung Kim, Mark-Oliver Stehr, Carolyn L. Talcot...