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Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization ...
The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian infer ...
We introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from v ...
Energy-based detection and estimation are crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present novel Gaussian approximations that are applicable to general energy-based source detection and localization proble ...
Background: Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes) is expensive. It is thus desirable to use an identification ...
In this paper, we investigate the approach of comparing two different parallel streams of phoneme posterior probability estimates for OOV word detection. The first phoneme posterior probability stream is estimated using only the knowledge of short-term acou ...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties ...
In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carrie ...
In this paper we propose the use of infinite models for the clustering of speakers. Speaker segmentation is obtained trough a Dirichlet Process Mixture (DPM) model which can be interpreted as a flexible model with an infinite a priori number of components. ...
In this paper we propose the use of infinite models for the clustering of speakers. Speaker segmentation is obtained trough a Dirichlet Process Mixture (DPM) model which can be interpreted as a flexible model with an infinite a priori number of components. ...
