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ICML
2007
IEEE
14 years 8 months ago
Most likely heteroscedastic Gaussian process regression
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
Kristian Kersting, Christian Plagemann, Patrick Pf...
TOIS
2010
128views more  TOIS 2010»
13 years 6 months ago
Learning author-topic models from text corpora
We propose a new unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a...
Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L...
CCS
2009
ACM
13 years 11 months ago
The bayesian traffic analysis of mix networks
This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network ar...
Carmela Troncoso, George Danezis
WSC
2007
13 years 10 months ago
Transformations for accelerating MCMC simulations with broken ergodicity
A new approach for overcoming broken ergodicity in Markov Chain Monte Carlo (MCMC) simulations of complex systems is described. The problem of broken ergodicity is often present i...
Mark Fleischer
CVPR
2009
IEEE
1216views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Marked Point Processes for Crowd Counting
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placem...
Robert T. Collins, Weina Ge