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» A Monte-Carlo AIXI Approximation
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IJCNN
2000
IEEE
13 years 12 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
ICCV
1999
IEEE
13 years 11 months ago
Bayesian Structure from Motion
:We formulate structure from motion as a Bayesian inference problem, and use a Markov chain Monte Carlo sampler to sample the posterior on this problem. This results in a method th...
David A. Forsyth, Sergey Ioffe, John A. Haddon
ICIP
2010
IEEE
13 years 5 months ago
Bayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation
Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffus...
Siming Wei, Jing Hua, Jiajun Bu, Chun Chen, Yizhou...
SAGA
2005
Springer
14 years 29 days ago
Dynamic Facility Location with Stochastic Demands
Abstract. In this paper, a Stochastic Dynamic Facility Location Problem (SDFLP) is formulated. In the first part, an exact solution method based on stochastic dynamic programming ...
Martin Romauch, Richard F. Hartl
ICPR
2008
IEEE
14 years 1 months ago
Approximation of salient contours in cluttered scenes
This paper proposes a new approach to describe the salient contours in cluttered scenes. No need to do the preprocessing, such as edge detection, we directly use a set of random s...
Rui Huang, Nong Sang, Qiling Tang