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Fast Exact Bayesian Inference for High-Dimensional Models

Joao Filipe Ferreira, Pablo Lanillos, Jorge Dias
AP4ISR team, Institute of Systems and Robotics (ISR) Dept. of Electrical & Computer Eng., University of Coimbra. Pinhal de Marrocos, Polo II, 3030-290 COIMBRA, Portugal
Workshop on Unconventional computing for Bayesian inference in Intelligent Robots and Systems (IROS), 2015

@inproceedings{ferreira2015fast,

   title={Fast Exact Bayesian Inference for High-Dimensional Models},

   author={Ferreira, JF and Lanillos, P and Dias, J},

   booktitle={Workshop on Unconventional computing for Bayesian inference (UCBI), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

   year={2015}

}

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In this text, we present the principles that allow the tractable implementation of exact inference processes concerning a group of widespread classes of Bayesian generative models, which have until recently been deemed as intractable whenever formulated using high-dimensional joint distributions. We will demonstrate the usefulness of such a principled approach with an example of real-time OpenCL implementation using GPUs of a full-fledged, computer vision-based model to estimate gaze direction in human-robot interaction (HRI).
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